Add tables

fev-leaderboard-app.py

@@ -2,9 +2,8 @@ import streamlit as st
 
 pages = [
     st.Page("pages/fev_bench.py", title="fev-bench", icon=":material/trophy:"),
-    st.Page("pages/chronos_bench_ii.py", title="Chronos Benchmark II", icon=":material/trophy:"),
     st.Page("pages/about.py", title="About", icon=":material/info:"),
 ]
 
 page = st.navigation(pages)
-page.run()
+page.run()

pages/about.py

@@ -1,10 +1,10 @@
 import streamlit as st
 
 ABOUT_LEADERBOARD = """
-## About `fev`
+## About
 
-**fev** is a lightweight wrapper around the 🤗 [datasets](https://huggingface.co/docs/datasets/en/index) library designed to streamline
- time series forecasting model benchmarking.
+[**fev**](https://github.com/autogluon/fev) is a lightweight wrapper around the 🤗 [datasets](https://huggingface.co/docs/datasets/en/index) library designed to streamline
+ benchmarking of time series forecasting models.
 
 ### 📚 Resources
 - **Documentation**: [Official docs](https://autogluon.github.io/fev/latest/)

pages/fev_bench.py

@@ -1,9 +1,8 @@
 import sys
 from pathlib import Path
 
-sys.path.append(str(Path(__file__).parent.parent))
+sys.path.append(str(Path(__file__).parent))
 
-import fev
 import pandas as pd
 import streamlit as st
 from streamlit.elements.lib.column_types import ColumnConfig
@@ -17,8 +16,8 @@ from src.strings import (
     get_pivot_legend,
 )
 from src.utils import (
+    COLORS,
     construct_pairwise_chart,
-    construct_pivot_table,
     format_leaderboard,
     format_metric_name,
     get_metric_description,
@@ -27,56 +26,26 @@ from src.utils import (
 st.set_page_config(layout="wide", page_title="fev leaderboard", page_icon=":material/trophy:")
 
 TITLE = "<h1 style='text-align: center; font-size: 350%;'>fev-bench</h1>"
-BASELINE_MODEL = "Seasonal Naive"
-LEAKAGE_IMPUTATION_MODEL = "Chronos-Bolt"
 SORT_COL = "win_rate"
-N_RESAMPLES_FOR_CI = 1000
-TOP_K_MODELS_TO_PLOT = 15
 AVAILABLE_METRICS = ["SQL", "MASE", "WQL", "WAPE"]
 
 
 @st.cache_data()
-def load_summaries():
-    summaries = pd.read_csv("summaries.csv")
-    return summaries
+def get_leaderboard(metric_name: str) -> pd.DataFrame:
+    return pd.read_csv(f"tables/leaderboard_{metric_name}.csv")
 
 
 @st.cache_data()
-def get_leaderboard(metric_name: str) -> pd.DataFrame:
-    summaries = load_summaries()
-    lb = fev.analysis.leaderboard(
-        summaries=summaries,
-        metric_column=metric_name,
-        missing_strategy="impute",
-        baseline_model=BASELINE_MODEL,
-        leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-    )
-    lb = lb.astype("float64").reset_index()
-
-    lb["skill_score"] = lb["skill_score"] * 100
-    lb["win_rate"] = lb["win_rate"] * 100
-    lb["num_failures"] = lb["num_failures"] / summaries["task_name"].nunique() * 100
-    return lb
+def get_pairwise(metric_name: str) -> pd.DataFrame:
+    return pd.read_csv(f"tables/pairwise_{metric_name}.csv")
 
 
 @st.cache_data()
-def get_pairwise(metric_name: str, included_models: list[str]) -> pd.DataFrame:
-    if BASELINE_MODEL not in included_models:
-        included_models = included_models + [BASELINE_MODEL]
-    summaries = load_summaries()
-    return (
-        fev.analysis.pairwise_comparison(
-            summaries,
-            included_models=included_models,
-            metric_column=metric_name,
-            baseline_model=BASELINE_MODEL,
-            missing_strategy="impute",
-            n_resamples=N_RESAMPLES_FOR_CI,
-            leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-        )
-        .round(3)
-        .reset_index()
-    )
+def get_pivot_table(metric_name: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    pivot_df = pd.read_csv(f"tables/pivot_{metric_name}.csv")
+    baseline_imputed = pd.read_csv(f"tables/pivot_{metric_name}_baseline_imputed.csv")
+    leakage_imputed = pd.read_csv(f"tables/pivot_{metric_name}_leakage_imputed.csv")
+    return pivot_df, baseline_imputed, leakage_imputed
 
 
 with st.sidebar:
@@ -89,15 +58,14 @@ with cols[1] as main_container:
     st.markdown(TITLE, unsafe_allow_html=True)
 
     metric_df = get_leaderboard(selected_metric).sort_values(by=SORT_COL, ascending=False)
-    top_k_models = metric_df.head(TOP_K_MODELS_TO_PLOT)["model_name"].tolist()
-    pairwise_df = get_pairwise(selected_metric, included_models=top_k_models)
+    pairwise_df = get_pairwise(selected_metric)
 
     st.markdown("## :material/trophy: Leaderboard", unsafe_allow_html=True)
     st.markdown(FEV_BENCHMARK_BASIC_INFO, unsafe_allow_html=True)
     df_styled = format_leaderboard(metric_df)
     st.dataframe(
         df_styled,
-        use_container_width=True,
+        width="stretch",
         hide_index=True,
         column_config={
             "model_name": ColumnConfig(label="Model Name", alignment="left"),
@@ -135,15 +103,39 @@ with cols[1] as main_container:
 
     st.markdown("## :material/table_chart: Results for individual tasks", unsafe_allow_html=True)
     with st.expander("Show detailed results"):
-        st.markdown(get_pivot_legend(BASELINE_MODEL, LEAKAGE_IMPUTATION_MODEL), unsafe_allow_html=True)
-        st.dataframe(
-            construct_pivot_table(
-                summaries=load_summaries(),
-                metric_name=selected_metric,
-                baseline_model=BASELINE_MODEL,
-                leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
-            )
-        )
+        st.markdown(get_pivot_legend("Seasonal Naive", "Chronos-Bolt"), unsafe_allow_html=True)
+        pivot_df, baseline_imputed, leakage_imputed = get_pivot_table(selected_metric)
+        pivot_df = pivot_df.set_index("Task name")
+        baseline_imputed = baseline_imputed.set_index("Task name")
+        leakage_imputed = leakage_imputed.set_index("Task name")
+
+        def style_pivot_table(errors, is_baseline_imputed, is_leakage_imputed):
+            rank_colors = {1: COLORS["gold"], 2: COLORS["silver"], 3: COLORS["bronze"]}
+
+            def highlight_by_position(styler):
+                for row_idx in errors.index:
+                    row_ranks = errors.loc[row_idx].rank(method="min")
+                    for col_idx in errors.columns:
+                        rank = row_ranks[col_idx]
+                        style_parts = []
+                        if rank <= 3:
+                            style_parts.append(f"background-color: {rank_colors[rank]}")
+                        if is_leakage_imputed.loc[row_idx, col_idx]:
+                            style_parts.append(f"color: {COLORS['leakage_impute']}")
+                        elif is_baseline_imputed.loc[row_idx, col_idx]:
+                            style_parts.append(f"color: {COLORS['failure_impute']}")
+                        elif not style_parts:
+                            style_parts.append(f"color: {COLORS['text_default']}")
+                        if style_parts:
+                            styler = styler.map(
+                                lambda x, s="; ".join(style_parts): s,
+                                subset=pd.IndexSlice[row_idx:row_idx, col_idx:col_idx],
+                            )
+                return styler
+
+            return highlight_by_position(errors.style).format(precision=3)
+
+        st.dataframe(style_pivot_table(pivot_df, baseline_imputed, leakage_imputed))
 
     st.divider()
     st.markdown("### :material/format_quote: Citation", unsafe_allow_html=True)

requirements.txt

@@ -1,4 +1,3 @@
-pandas
 matplotlib
 numpy
 pandas

src/streamlit_app.py

@@ -1,40 +1,9 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
+pages = [
+    st.Page("../pages/fev_bench.py", title="fev-bench", icon=":material/trophy:"),
+    st.Page("../pages/about.py", title="About", icon=":material/info:"),
+]
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+page = st.navigation(pages)
+page.run()

src/strings.py

@@ -8,7 +8,7 @@ LEGEND = """
 """
 
 TABLE_INFO = f"""
-The leaderboard summarizes the performance of all models evaluated on the 100 tasks comprising `fev-bench`.
+The leaderboard summarizes the performance of all models evaluated on the 100 tasks comprising **fev-bench**. More details available in the [paper](https://arxiv.org/abs/2509.26468).
 
 Model names are colored by type: <span style='color: {COLORS["dl_text"]}; font-weight: bold;'>Deep Learning</span> and <span style='color: {COLORS["st_text"]}; font-weight: bold;'>Statistical</span>.
 
@@ -20,7 +20,7 @@ The full matrix $E_{{rj}}$ with the error of each model $j$ on task $r$ is avail
 
 * **Median runtime (s)**: Median end-to-end time (training + prediction across all evaluation windows) in seconds. Note that inference times depend on hardware, batch sizes, and implementation details, so these serve as a rough guide rather than definitive performance benchmarks.
 
-* **Leakage (%)**: For zero-shot models, percentage of benchmark datasets included in the model's training corpus. For zero-shot models, results for tasks with reported overlap are replaced with Chronos-Bolt (Base) performance to prevent data leakage.
+* **Leakage (%)**: For zero-shot models, percentage of benchmark datasets included in the model's training corpus. Results for tasks with reported overlap are replaced with Chronos-Bolt (Base) performance to prevent data leakage.
 
 * **Failed tasks (%)**: Percentage of tasks where the model failed to produce a forecast. Results for failed tasks are replaced with Seasonal Naive performance.
 
@@ -44,7 +44,7 @@ Results for various forecasting models on 100 tasks of the **fev-bench** benchma
 FEV_BENCHMARK_DETAILS = f"""
 {TABLE_INFO}
 
-Task definitions and the detailed results are available on [GitHub](https://github.com/autogluon/fev/tree/main/benchmarks/). More information for the datasets is available in [Table 3 of the paper](https://arxiv.org/).
+Task definitions and the detailed results are available on [GitHub](https://github.com/autogluon/fev/tree/main/benchmarks/). Datasets used for evaluation are available on [Hugging Face](https://huggingface.co/datasets/autogluon/fev_datasets).
 """
 
 CITATION_HEADER = """

src/utils.py

@@ -7,7 +7,8 @@ import pandas.io.formats.style
 
 COLORS = {
     "dl_text": "#5A7FA5",
-    "st_text": "#666666",
+    "st_text": "#A5795A",
+    # "st_text": "#666666",
     "bar_fill": "#8d5eb7",
     "error_bar": "#222222",
     "point": "#111111",
@@ -66,19 +67,19 @@ MODEL_CONFIG = {
 ALL_METRICS = {
     "SQL": (
         "SQL: Scaled Quantile Loss",
-        "The [Scaled Quantile Loss (SQL)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.SQL) is a scale-invariant metric for evaluating probabilistic forecasts.",
+        "The [Scaled Quantile Loss (SQL)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.SQL) is a **scale-invariant** metric for evaluating **probabilistic** forecasts.",
     ),
     "MASE": (
         "MASE: Mean Absolute Scaled Error",
-        "The [Mean Absolute Scaled Error (MASE)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.MASE) is a scale-invariant metric for evaluating point forecasts.",
+        "The [Mean Absolute Scaled Error (MASE)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.MASE) is a **scale-invariant** metric for evaluating **point** forecasts.",
     ),
     "WQL": (
         "WQL: Weighted Quantile Loss",
-        "The [Weighted Quantile Loss (WQL)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.WQL), is a scale-dependent metric for evaluating probabilistic forecasts.",
+        "The [Weighted Quantile Loss (WQL)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.WQL), is a **scale-dependent** metric for evaluating **probabilistic** forecasts.",
     ),
     "WAPE": (
         "WAPE: Weighted Absolute Percentage Error",
-        "The [Weighted Absolute Percentage Error (WAPE)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.WAPE) is a scale-dependent metric for evaluating point forecasts.",
+        "The [Weighted Absolute Percentage Error (WAPE)](https://auto.gluon.ai/dev/tutorials/timeseries/forecasting-metrics.html#autogluon.timeseries.metrics.WAPE) is a **scale-dependent** metric for evaluating **point** forecasts.",
     ),
 }
 
@@ -146,9 +147,11 @@ def format_leaderboard(df: pd.DataFrame):
             "link",
         ]
     ]
-    return df.style.map(highlight_model_type_color, subset=["model_name"]).map(
-        lambda x: "font-weight: bold", subset=["zero_shot"]
-    ).apply(lambda x: ['background-color: #f8f9fa' if i % 2 == 1 else '' for i in range(len(x))], axis=0)
+    return (
+        df.style.map(highlight_model_type_color, subset=["model_name"])
+        .map(lambda x: "font-weight: bold", subset=["zero_shot"])
+        .apply(lambda x: ["background-color: #f8f9fa" if i % 2 == 1 else "" for i in range(len(x))], axis=0)
+    )
 
 
 def construct_bar_chart(df: pd.DataFrame, col: str, metric_name: str):
@@ -242,6 +245,33 @@ def construct_pairwise_chart(df: pd.DataFrame, col: str, metric_name: str):
     )
 
 
+def construct_pivot_table_from_df(errors: pd.DataFrame, metric_name: str) -> pd.io.formats.style.Styler:
+    """Construct styled pivot table from precomputed DataFrame."""
+
+    def highlight_by_position(styler):
+        rank_colors = {1: COLORS["gold"], 2: COLORS["silver"], 3: COLORS["bronze"]}
+
+        for row_idx in errors.index:
+            row_ranks = errors.loc[row_idx].rank(method="min")
+            for col_idx in errors.columns:
+                rank = row_ranks[col_idx]
+                style_parts = []
+
+                # Rank background colors
+                if rank <= 3:
+                    style_parts.append(f"background-color: {rank_colors[rank]}")
+                else:
+                    style_parts.append(f"color: {COLORS['text_default']}")
+
+                if style_parts:
+                    styler = styler.map(
+                        lambda x, s="; ".join(style_parts): s, subset=pd.IndexSlice[row_idx:row_idx, col_idx:col_idx]
+                    )
+        return styler
+
+    return highlight_by_position(errors.style).format(precision=3)
+
+
 def construct_pivot_table(
     summaries: pd.DataFrame, metric_name: str, baseline_model: str, leakage_imputation_model: str
 ) -> pd.io.formats.style.Styler:

tables/leaderboard_MASE.csv

@@ -0,0 +1,15 @@
+model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
+TiRex,80.5,30.012261671412087,0.0,1.4030189444999999,0.01,0.0
+TimesFM-2.5,79.92307692307693,30.343506289096023,0.0,117.557863139,0.08,0.0
+Toto-1.0,69.92307692307692,28.213885409107164,0.0,90.676829282,0.08,0.0
+Moirai-2.0,65.15384615384616,27.25905867510979,0.0,2.5351729785000003,0.28,0.0
+Chronos-Bolt,64.84615384615384,26.51514785224567,0.0,0.9960156920000001,0.0,0.0
+TabPFN-TS,61.96153846153846,27.649834806479856,0.0,305.466367349,0.0,2.0
+Sundial-Base,56.730769230769226,24.746214191232585,0.0,35.620029862500004,0.01,0.0
+Stat. Ensemble,51.038461538461554,15.654207763455553,0.0,690.615290623,0.0,11.0
+AutoARIMA,39.03846153846154,11.239602667684679,0.0,186.7699845295,0.0,10.0
+AutoTheta,37.07692307692307,10.9884701559679,0.0,9.267665384499999,0.0,0.0
+AutoETS,34.88461538461539,2.258981670108584,0.0,17.004582018,0.0,3.0
+Seasonal Naive,22.34615384615384,0.0,0.0,2.3247850175,0.0,0.0
+Naive,20.576923076923077,-16.673948793089565,0.0,2.2371214229999996,0.0,0.0
+Drift,16.000000000000004,-18.13920384837251,0.0,2.1929671395000003,0.0,0.0

tables/leaderboard_SQL.csv

@@ -0,0 +1,15 @@
+model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
+TiRex,86.73076923076923,42.57610775202675,0.0,1.4030189444999999,0.01,0.0
+TimesFM-2.5,82.07692307692308,42.307780979852375,0.0,117.557863139,0.08,0.0
+Toto-1.0,73.84615384615384,40.7460359139889,0.0,90.676829282,0.08,0.0
+Chronos-Bolt,68.84615384615384,38.892961600022936,0.0,0.9960156920000001,0.0,0.0
+Moirai-2.0,68.76923076923076,39.332013785815214,0.0,2.5351729785000003,0.28,0.0
+TabPFN-TS,66.88461538461539,39.58671179038912,0.0,305.466367349,0.0,2.0
+Sundial-Base,49.19230769230769,33.42287717226134,0.0,35.620029862500004,0.01,0.0
+Stat. Ensemble,48.65384615384615,20.161731427800046,0.0,690.615290623,0.0,11.0
+AutoARIMA,43.5,20.561948549632326,0.0,186.7699845295,0.0,10.0
+AutoETS,35.80769230769231,-26.818526760288375,0.0,17.004582018,0.0,3.0
+AutoTheta,29.153846153846153,5.457380397818312,0.0,9.267665384499999,0.0,0.0
+Seasonal Naive,21.730769230769226,0.0,0.0,2.3247850175,0.0,0.0
+Naive,14.884615384615389,-45.398988807164976,0.0,2.2371214229999996,0.0,0.0
+Drift,9.923076923076923,-45.77585379444895,0.0,2.1929671395000003,0.0,0.0

tables/leaderboard_WAPE.csv

@@ -0,0 +1,15 @@
+model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
+TimesFM-2.5,79.0769230769231,33.818136330484805,0.0,117.557863139,0.08,0.0
+TiRex,78.8076923076923,33.56017122067458,0.0,1.4030189444999999,0.01,0.0
+Toto-1.0,70.30769230769232,31.488756179777678,0.0,90.676829282,0.08,0.0
+TabPFN-TS,67.88461538461537,33.36123166513091,0.0,305.466367349,0.0,2.0
+Moirai-2.0,65.61538461538461,30.651048841637785,0.0,2.5351729785000003,0.28,0.0
+Chronos-Bolt,64.92307692307692,29.770064235526473,0.0,0.9960156920000001,0.0,0.0
+Sundial-Base,54.576923076923066,27.263435775595426,0.0,35.620029862500004,0.01,0.0
+Stat. Ensemble,50.26923076923077,17.693885547849597,0.0,690.615290623,0.0,11.0
+AutoARIMA,37.03846153846154,13.271687847205381,0.0,186.7699845295,0.0,10.0
+AutoETS,36.346153846153854,4.328689341465408,0.0,17.004582018,0.0,3.0
+AutoTheta,34.00000000000001,13.794755020224814,0.0,9.267665384499999,0.0,0.0
+Naive,23.96153846153846,-6.11148881462471,0.0,2.2371214229999996,0.0,0.0
+Seasonal Naive,20.346153846153847,0.0,0.0,2.3247850175,0.0,0.0
+Drift,16.84615384615385,-8.604851625817744,0.0,2.1929671395000003,0.0,0.0

tables/leaderboard_WQL.csv

@@ -0,0 +1,15 @@
+model_name,win_rate,skill_score,median_training_time_s,median_inference_time_s,training_corpus_overlap,num_failures
+TiRex,84.65384615384616,46.66550397812589,0.0,1.4030189444999999,0.01,0.0
+TimesFM-2.5,82.53846153846153,46.80188214969573,0.0,117.557863139,0.08,0.0
+Toto-1.0,73.92307692307693,45.00088243993733,0.0,90.676829282,0.08,0.0
+TabPFN-TS,71.88461538461539,45.822378843833036,0.0,305.466367349,0.0,2.0
+Moirai-2.0,70.15384615384615,43.864523387242194,0.0,2.5351729785000003,0.28,0.0
+Chronos-Bolt,69.0,43.187343947848866,0.0,0.9960156920000001,0.0,0.0
+Sundial-Base,50.42307692307693,37.43731640370259,0.0,35.620029862500004,0.01,0.0
+Stat. Ensemble,47.1923076923077,21.795752415252334,0.0,690.615290623,0.0,11.0
+AutoARIMA,42.884615384615394,23.401617100945593,0.0,186.7699845295,0.0,10.0
+AutoETS,33.42307692307693,-27.026777935471568,0.0,17.004582018,0.0,3.0
+AutoTheta,28.61538461538461,7.846425960422055,0.0,9.267665384499999,0.0,0.0
+Seasonal Naive,20.807692307692307,0.0,0.0,2.3247850175,0.0,0.0
+Naive,14.653846153846157,-39.121433468308894,0.0,2.2371214229999996,0.0,0.0
+Drift,9.846153846153847,-40.05851008470427,0.0,2.1929671395000003,0.0,0.0

tables/pairwise_MASE.csv

@@ -0,0 +1,197 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
+TiRex,TimesFM-2.5,0.465,0.37,0.565,-0.005,-0.021,0.012
+TiRex,Toto-1.0,0.615,0.52,0.71,0.025,0.003,0.046
+TiRex,Moirai-2.0,0.765,0.685,0.845,0.038,0.021,0.055
+TiRex,Chronos-Bolt,0.765,0.68,0.845,0.048,0.029,0.067
+TiRex,TabPFN-TS,0.69,0.6,0.78,0.033,-0.01,0.074
+TiRex,Sundial-Base,0.805,0.72,0.88,0.07,0.036,0.104
+TiRex,Stat. Ensemble,0.81,0.73,0.89,0.17,0.132,0.21
+TiRex,AutoARIMA,0.9,0.84,0.95,0.211,0.175,0.253
+TiRex,AutoTheta,0.94,0.89,0.98,0.214,0.18,0.249
+TiRex,AutoETS,0.84,0.77,0.91,0.284,0.225,0.345
+TiRex,Seasonal Naive,0.97,0.94,1.0,0.3,0.264,0.339
+TiRex,Naive,0.96,0.92,0.99,0.4,0.344,0.457
+TiRex,Drift,0.94,0.89,0.98,0.408,0.347,0.462
+TimesFM-2.5,TiRex,0.535,0.435,0.63,0.005,-0.012,0.021
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
+TimesFM-2.5,Toto-1.0,0.58,0.495,0.675,0.03,0.007,0.054
+TimesFM-2.5,Moirai-2.0,0.73,0.645,0.81,0.042,0.024,0.062
+TimesFM-2.5,Chronos-Bolt,0.73,0.65,0.81,0.052,0.031,0.073
+TimesFM-2.5,TabPFN-TS,0.68,0.59,0.77,0.037,-0.009,0.082
+TimesFM-2.5,Sundial-Base,0.815,0.74,0.89,0.074,0.043,0.107
+TimesFM-2.5,Stat. Ensemble,0.81,0.73,0.88,0.174,0.128,0.219
+TimesFM-2.5,AutoARIMA,0.88,0.81,0.94,0.215,0.175,0.259
+TimesFM-2.5,AutoTheta,0.91,0.85,0.96,0.217,0.18,0.257
+TimesFM-2.5,AutoETS,0.85,0.78,0.92,0.287,0.229,0.347
+TimesFM-2.5,Seasonal Naive,0.97,0.93,1.0,0.303,0.269,0.343
+TimesFM-2.5,Naive,0.97,0.93,1.0,0.403,0.343,0.458
+TimesFM-2.5,Drift,0.93,0.88,0.98,0.41,0.346,0.47
+Toto-1.0,TiRex,0.385,0.29,0.48,-0.026,-0.048,-0.003
+Toto-1.0,TimesFM-2.5,0.42,0.325,0.505,-0.031,-0.057,-0.007
+Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
+Toto-1.0,Moirai-2.0,0.58,0.49,0.67,0.013,-0.008,0.034
+Toto-1.0,Chronos-Bolt,0.56,0.46,0.645,0.023,-0.005,0.048
+Toto-1.0,TabPFN-TS,0.58,0.49,0.67,0.008,-0.043,0.055
+Toto-1.0,Sundial-Base,0.675,0.585,0.765,0.046,0.007,0.088
+Toto-1.0,Stat. Ensemble,0.73,0.64,0.81,0.149,0.102,0.193
+Toto-1.0,AutoARIMA,0.81,0.73,0.88,0.191,0.149,0.237
+Toto-1.0,AutoTheta,0.82,0.74,0.89,0.194,0.151,0.235
+Toto-1.0,AutoETS,0.79,0.71,0.86,0.266,0.203,0.326
+Toto-1.0,Seasonal Naive,0.91,0.85,0.96,0.282,0.241,0.327
+Toto-1.0,Naive,0.94,0.88,0.98,0.385,0.32,0.442
+Toto-1.0,Drift,0.89,0.82,0.94,0.392,0.328,0.451
+Moirai-2.0,TiRex,0.235,0.155,0.315,-0.039,-0.059,-0.021
+Moirai-2.0,TimesFM-2.5,0.27,0.19,0.355,-0.044,-0.066,-0.025
+Moirai-2.0,Toto-1.0,0.42,0.33,0.51,-0.013,-0.036,0.008
+Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
+Moirai-2.0,Chronos-Bolt,0.55,0.465,0.64,0.01,-0.012,0.032
+Moirai-2.0,TabPFN-TS,0.52,0.43,0.62,-0.005,-0.054,0.038
+Moirai-2.0,Sundial-Base,0.685,0.595,0.775,0.033,-0.004,0.067
+Moirai-2.0,Stat. Ensemble,0.73,0.64,0.82,0.138,0.09,0.18
+Moirai-2.0,AutoARIMA,0.81,0.73,0.88,0.18,0.138,0.228
+Moirai-2.0,AutoTheta,0.85,0.77,0.91,0.183,0.144,0.223
+Moirai-2.0,AutoETS,0.76,0.67,0.83,0.256,0.192,0.32
+Moirai-2.0,Seasonal Naive,0.87,0.8,0.93,0.273,0.236,0.314
+Moirai-2.0,Naive,0.91,0.85,0.96,0.377,0.316,0.433
+Moirai-2.0,Drift,0.86,0.79,0.92,0.384,0.32,0.441
+Chronos-Bolt,TiRex,0.235,0.155,0.32,-0.05,-0.072,-0.03
+Chronos-Bolt,TimesFM-2.5,0.27,0.19,0.35,-0.055,-0.079,-0.032
+Chronos-Bolt,Toto-1.0,0.44,0.355,0.54,-0.024,-0.051,0.005
+Chronos-Bolt,Moirai-2.0,0.45,0.36,0.535,-0.01,-0.033,0.012
+Chronos-Bolt,Chronos-Bolt,0.5,0.5,0.5,0.0,0.0,0.0
+Chronos-Bolt,TabPFN-TS,0.52,0.42,0.61,-0.016,-0.062,0.025
+Chronos-Bolt,Sundial-Base,0.605,0.51,0.695,0.024,-0.014,0.057
+Chronos-Bolt,Stat. Ensemble,0.7,0.61,0.79,0.129,0.084,0.174
+Chronos-Bolt,AutoARIMA,0.84,0.76,0.9,0.172,0.132,0.215
+Chronos-Bolt,AutoTheta,0.86,0.79,0.92,0.174,0.137,0.212
+Chronos-Bolt,AutoETS,0.77,0.68,0.85,0.248,0.184,0.315
+Chronos-Bolt,Seasonal Naive,0.9,0.84,0.95,0.265,0.226,0.304
+Chronos-Bolt,Naive,0.94,0.89,0.98,0.37,0.315,0.423
+Chronos-Bolt,Drift,0.9,0.83,0.95,0.378,0.319,0.435
+TabPFN-TS,TiRex,0.31,0.22,0.4,-0.034,-0.08,0.01
+TabPFN-TS,TimesFM-2.5,0.32,0.23,0.41,-0.039,-0.09,0.009
+TabPFN-TS,Toto-1.0,0.42,0.33,0.51,-0.008,-0.058,0.041
+TabPFN-TS,Moirai-2.0,0.48,0.38,0.57,0.005,-0.039,0.051
+TabPFN-TS,Chronos-Bolt,0.48,0.39,0.58,0.015,-0.026,0.058
+TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
+TabPFN-TS,Sundial-Base,0.53,0.43,0.63,0.039,-0.001,0.078
+TabPFN-TS,Stat. Ensemble,0.645,0.545,0.735,0.142,0.089,0.19
+TabPFN-TS,AutoARIMA,0.75,0.67,0.83,0.185,0.133,0.241
+TabPFN-TS,AutoTheta,0.75,0.66,0.83,0.187,0.14,0.235
+TabPFN-TS,AutoETS,0.72,0.63,0.81,0.26,0.183,0.332
+TabPFN-TS,Seasonal Naive,0.91,0.855,0.96,0.276,0.231,0.321
+TabPFN-TS,Naive,0.87,0.8,0.93,0.38,0.318,0.442
+TabPFN-TS,Drift,0.87,0.8,0.94,0.388,0.321,0.45
+Sundial-Base,TiRex,0.195,0.12,0.28,-0.075,-0.115,-0.037
+Sundial-Base,TimesFM-2.5,0.185,0.11,0.26,-0.08,-0.12,-0.044
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+Sundial-Base,TabPFN-TS,0.47,0.37,0.57,-0.04,-0.085,0.001
+Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
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+Sundial-Base,AutoARIMA,0.76,0.67,0.84,0.152,0.098,0.203
+Sundial-Base,AutoTheta,0.77,0.68,0.85,0.155,0.109,0.199
+Sundial-Base,AutoETS,0.71,0.61,0.8,0.23,0.152,0.3
+Sundial-Base,Seasonal Naive,0.88,0.81,0.94,0.247,0.206,0.288
+Sundial-Base,Naive,0.88,0.8,0.94,0.355,0.289,0.417
+Sundial-Base,Drift,0.83,0.75,0.9,0.363,0.291,0.428
+Stat. Ensemble,TiRex,0.19,0.11,0.27,-0.205,-0.265,-0.152
+Stat. Ensemble,TimesFM-2.5,0.19,0.12,0.27,-0.211,-0.28,-0.147
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+Stat. Ensemble,Sundial-Base,0.34,0.24,0.44,-0.121,-0.19,-0.054
+Stat. Ensemble,Stat. Ensemble,0.5,0.5,0.5,0.0,0.0,0.0
+Stat. Ensemble,AutoARIMA,0.64,0.545,0.73,0.05,0.02,0.09
+Stat. Ensemble,AutoTheta,0.75,0.67,0.83,0.052,0.025,0.082
+Stat. Ensemble,AutoETS,0.795,0.72,0.87,0.137,0.081,0.194
+Stat. Ensemble,Seasonal Naive,0.795,0.725,0.865,0.157,0.116,0.203
+Stat. Ensemble,Naive,0.85,0.78,0.92,0.277,0.218,0.346
+Stat. Ensemble,Drift,0.89,0.82,0.95,0.286,0.225,0.35
+AutoARIMA,TiRex,0.1,0.05,0.16,-0.268,-0.339,-0.212
+AutoARIMA,TimesFM-2.5,0.12,0.06,0.19,-0.274,-0.349,-0.213
+AutoARIMA,Toto-1.0,0.19,0.12,0.27,-0.236,-0.311,-0.175
+AutoARIMA,Moirai-2.0,0.19,0.12,0.27,-0.22,-0.295,-0.16
+AutoARIMA,Chronos-Bolt,0.16,0.1,0.24,-0.208,-0.274,-0.152
+AutoARIMA,TabPFN-TS,0.25,0.17,0.33,-0.227,-0.317,-0.153
+AutoARIMA,Sundial-Base,0.24,0.16,0.33,-0.179,-0.254,-0.108
+AutoARIMA,Stat. Ensemble,0.36,0.27,0.455,-0.052,-0.099,-0.02
+AutoARIMA,AutoARIMA,0.5,0.5,0.5,0.0,0.0,0.0
+AutoARIMA,AutoTheta,0.55,0.45,0.64,0.003,-0.039,0.039
+AutoARIMA,AutoETS,0.595,0.5,0.69,0.092,0.024,0.162
+AutoARIMA,Seasonal Naive,0.75,0.665,0.83,0.112,0.068,0.161
+AutoARIMA,Naive,0.77,0.69,0.85,0.239,0.169,0.312
+AutoARIMA,Drift,0.8,0.72,0.87,0.249,0.174,0.324
+AutoTheta,TiRex,0.06,0.02,0.11,-0.272,-0.332,-0.22
+AutoTheta,TimesFM-2.5,0.09,0.04,0.15,-0.278,-0.345,-0.219
+AutoTheta,Toto-1.0,0.18,0.11,0.26,-0.24,-0.307,-0.178
+AutoTheta,Moirai-2.0,0.15,0.09,0.23,-0.224,-0.287,-0.168
+AutoTheta,Chronos-Bolt,0.14,0.08,0.21,-0.211,-0.269,-0.159
+AutoTheta,TabPFN-TS,0.25,0.17,0.34,-0.23,-0.307,-0.163
+AutoTheta,Sundial-Base,0.23,0.15,0.32,-0.183,-0.249,-0.122
+AutoTheta,Stat. Ensemble,0.25,0.17,0.33,-0.055,-0.09,-0.025
+AutoTheta,AutoARIMA,0.45,0.36,0.55,-0.003,-0.041,0.037
+AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
+AutoTheta,AutoETS,0.59,0.5,0.69,0.089,0.026,0.155
+AutoTheta,Seasonal Naive,0.78,0.7,0.86,0.11,0.069,0.149
+AutoTheta,Naive,0.83,0.75,0.9,0.237,0.181,0.301
+AutoTheta,Drift,0.82,0.74,0.9,0.247,0.189,0.31
+AutoETS,TiRex,0.16,0.09,0.23,-0.397,-0.526,-0.291
+AutoETS,TimesFM-2.5,0.15,0.08,0.22,-0.403,-0.532,-0.298
+AutoETS,Toto-1.0,0.21,0.14,0.29,-0.362,-0.484,-0.254
+AutoETS,Moirai-2.0,0.24,0.17,0.33,-0.344,-0.471,-0.237
+AutoETS,Chronos-Bolt,0.23,0.15,0.32,-0.33,-0.459,-0.225
+AutoETS,TabPFN-TS,0.28,0.19,0.37,-0.351,-0.498,-0.225
+AutoETS,Sundial-Base,0.29,0.2,0.39,-0.299,-0.428,-0.179
+AutoETS,Stat. Ensemble,0.205,0.13,0.28,-0.159,-0.241,-0.088
+AutoETS,AutoARIMA,0.405,0.31,0.5,-0.101,-0.194,-0.024
+AutoETS,AutoTheta,0.41,0.31,0.5,-0.098,-0.183,-0.027
+AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
+AutoETS,Seasonal Naive,0.635,0.54,0.72,0.023,-0.071,0.103
+AutoETS,Naive,0.64,0.55,0.73,0.162,0.086,0.251
+AutoETS,Drift,0.68,0.59,0.77,0.173,0.098,0.258
+Seasonal Naive,TiRex,0.03,0.0,0.06,-0.429,-0.512,-0.359
+Seasonal Naive,TimesFM-2.5,0.03,0.0,0.07,-0.436,-0.523,-0.368
+Seasonal Naive,Toto-1.0,0.09,0.04,0.15,-0.393,-0.485,-0.318
+Seasonal Naive,Moirai-2.0,0.13,0.07,0.2,-0.375,-0.458,-0.308
+Seasonal Naive,Chronos-Bolt,0.1,0.05,0.16,-0.361,-0.437,-0.293
+Seasonal Naive,TabPFN-TS,0.09,0.04,0.145,-0.382,-0.472,-0.301
+Seasonal Naive,Sundial-Base,0.12,0.06,0.19,-0.329,-0.405,-0.259
+Seasonal Naive,Stat. Ensemble,0.205,0.135,0.275,-0.186,-0.255,-0.131
+Seasonal Naive,AutoARIMA,0.25,0.17,0.335,-0.127,-0.191,-0.073
+Seasonal Naive,AutoTheta,0.22,0.14,0.3,-0.123,-0.176,-0.074
+Seasonal Naive,AutoETS,0.365,0.28,0.46,-0.023,-0.115,0.067
+Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Seasonal Naive,Naive,0.575,0.48,0.665,0.143,0.065,0.223
+Seasonal Naive,Drift,0.7,0.6,0.79,0.154,0.067,0.238
+Naive,TiRex,0.04,0.01,0.08,-0.667,-0.842,-0.525
+Naive,TimesFM-2.5,0.03,0.0,0.07,-0.675,-0.846,-0.522
+Naive,Toto-1.0,0.06,0.02,0.12,-0.625,-0.792,-0.471
+Naive,Moirai-2.0,0.09,0.04,0.15,-0.604,-0.764,-0.463
+Naive,Chronos-Bolt,0.06,0.02,0.11,-0.588,-0.733,-0.46
+Naive,TabPFN-TS,0.13,0.07,0.2,-0.613,-0.791,-0.467
+Naive,Sundial-Base,0.12,0.06,0.2,-0.55,-0.716,-0.407
+Naive,Stat. Ensemble,0.15,0.08,0.22,-0.383,-0.529,-0.279
+Naive,AutoARIMA,0.23,0.15,0.31,-0.314,-0.454,-0.204
+Naive,AutoTheta,0.17,0.1,0.25,-0.311,-0.432,-0.222
+Naive,AutoETS,0.36,0.27,0.45,-0.194,-0.336,-0.094
+Naive,Seasonal Naive,0.425,0.335,0.52,-0.167,-0.288,-0.069
+Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Naive,Drift,0.81,0.73,0.89,0.012,-0.012,0.035
+Drift,TiRex,0.06,0.02,0.11,-0.688,-0.859,-0.531
+Drift,TimesFM-2.5,0.07,0.02,0.12,-0.696,-0.886,-0.53
+Drift,Toto-1.0,0.11,0.06,0.18,-0.646,-0.821,-0.487
+Drift,Moirai-2.0,0.14,0.08,0.21,-0.624,-0.789,-0.47
+Drift,Chronos-Bolt,0.1,0.05,0.17,-0.608,-0.77,-0.468
+Drift,TabPFN-TS,0.13,0.06,0.2,-0.633,-0.819,-0.473
+Drift,Sundial-Base,0.17,0.1,0.25,-0.57,-0.747,-0.411
+Drift,Stat. Ensemble,0.11,0.05,0.18,-0.401,-0.539,-0.291
+Drift,AutoARIMA,0.2,0.13,0.28,-0.331,-0.479,-0.21
+Drift,AutoTheta,0.18,0.1,0.26,-0.327,-0.45,-0.233
+Drift,AutoETS,0.32,0.23,0.41,-0.209,-0.347,-0.109
+Drift,Seasonal Naive,0.3,0.21,0.4,-0.181,-0.312,-0.071
+Drift,Naive,0.19,0.11,0.27,-0.013,-0.036,0.012
+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/pairwise_SQL.csv

@@ -0,0 +1,197 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
+TiRex,TimesFM-2.5,0.545,0.445,0.635,0.005,-0.012,0.021
+TiRex,Toto-1.0,0.685,0.595,0.775,0.031,0.009,0.052
+TiRex,Chronos-Bolt,0.835,0.76,0.905,0.06,0.042,0.08
+TiRex,Moirai-2.0,0.825,0.75,0.895,0.053,0.036,0.072
+TiRex,TabPFN-TS,0.72,0.63,0.81,0.049,0.005,0.092
+TiRex,Sundial-Base,0.905,0.85,0.96,0.137,0.107,0.169
+TiRex,Stat. Ensemble,0.92,0.86,0.97,0.281,0.232,0.328
+TiRex,AutoARIMA,0.95,0.9,0.99,0.277,0.237,0.32
+TiRex,AutoETS,0.92,0.86,0.97,0.524,0.417,0.626
+TiRex,AutoTheta,0.99,0.97,1.0,0.393,0.339,0.445
+TiRex,Seasonal Naive,1.0,1.0,1.0,0.426,0.379,0.479
+TiRex,Naive,0.99,0.97,1.0,0.605,0.547,0.653
+TiRex,Drift,0.99,0.97,1.0,0.606,0.545,0.655
+TimesFM-2.5,TiRex,0.455,0.365,0.555,-0.005,-0.021,0.012
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
+TimesFM-2.5,Toto-1.0,0.57,0.48,0.67,0.026,0.003,0.05
+TimesFM-2.5,Chronos-Bolt,0.73,0.645,0.81,0.056,0.035,0.077
+TimesFM-2.5,Moirai-2.0,0.75,0.67,0.825,0.049,0.028,0.071
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+TimesFM-2.5,Sundial-Base,0.925,0.875,0.97,0.133,0.105,0.162
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+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/pairwise_WAPE.csv

@@ -0,0 +1,197 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
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+TimesFM-2.5,Moirai-2.0,0.71,0.625,0.79,0.046,0.021,0.071
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+TimesFM-2.5,Sundial-Base,0.835,0.76,0.905,0.09,0.054,0.127
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+TimesFM-2.5,Naive,0.93,0.87,0.98,0.376,0.322,0.43
+TimesFM-2.5,Seasonal Naive,0.95,0.9,0.99,0.338,0.294,0.388
+TimesFM-2.5,Drift,0.92,0.86,0.97,0.391,0.338,0.446
+TiRex,TimesFM-2.5,0.495,0.4,0.58,-0.004,-0.021,0.013
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
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+TabPFN-TS,TiRex,0.35,0.26,0.44,-0.003,-0.055,0.043
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+TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
+TabPFN-TS,Moirai-2.0,0.52,0.42,0.62,0.039,-0.012,0.085
+TabPFN-TS,Chronos-Bolt,0.53,0.44,0.62,0.051,0.006,0.094
+TabPFN-TS,Sundial-Base,0.66,0.56,0.75,0.084,0.041,0.126
+TabPFN-TS,Stat. Ensemble,0.775,0.695,0.855,0.19,0.134,0.242
+TabPFN-TS,AutoARIMA,0.83,0.76,0.9,0.232,0.178,0.283
+TabPFN-TS,AutoETS,0.77,0.69,0.85,0.303,0.23,0.382
+TabPFN-TS,AutoTheta,0.84,0.76,0.9,0.227,0.18,0.277
+TabPFN-TS,Naive,0.91,0.85,0.96,0.372,0.316,0.432
+TabPFN-TS,Seasonal Naive,0.94,0.89,0.98,0.334,0.29,0.379
+TabPFN-TS,Drift,0.89,0.83,0.95,0.386,0.328,0.447
+Moirai-2.0,TimesFM-2.5,0.29,0.21,0.375,-0.048,-0.077,-0.021
+Moirai-2.0,TiRex,0.305,0.21,0.385,-0.044,-0.069,-0.023
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+Moirai-2.0,TabPFN-TS,0.48,0.38,0.58,-0.041,-0.093,0.012
+Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
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+Moirai-2.0,AutoETS,0.77,0.68,0.85,0.275,0.196,0.35
+Moirai-2.0,AutoTheta,0.88,0.81,0.94,0.196,0.147,0.243
+Moirai-2.0,Naive,0.87,0.8,0.93,0.346,0.29,0.403
+Moirai-2.0,Seasonal Naive,0.87,0.8,0.93,0.307,0.258,0.357
+Moirai-2.0,Drift,0.87,0.8,0.93,0.361,0.303,0.417
+Chronos-Bolt,TimesFM-2.5,0.26,0.18,0.34,-0.061,-0.088,-0.037
+Chronos-Bolt,TiRex,0.255,0.17,0.34,-0.057,-0.083,-0.033
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+Chronos-Bolt,TabPFN-TS,0.47,0.38,0.56,-0.054,-0.104,-0.006
+Chronos-Bolt,Moirai-2.0,0.44,0.355,0.52,-0.013,-0.038,0.011
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+Chronos-Bolt,Sundial-Base,0.635,0.54,0.73,0.034,-0.006,0.071
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+Chronos-Bolt,AutoETS,0.77,0.68,0.85,0.266,0.19,0.341
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+Chronos-Bolt,Naive,0.89,0.83,0.95,0.338,0.28,0.394
+Chronos-Bolt,Seasonal Naive,0.91,0.85,0.96,0.298,0.252,0.345
+Chronos-Bolt,Drift,0.9,0.84,0.95,0.353,0.296,0.41
+Sundial-Base,TimesFM-2.5,0.165,0.095,0.24,-0.099,-0.145,-0.057
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+Sundial-Base,Sundial-Base,0.5,0.5,0.5,0.0,0.0,0.0
+Sundial-Base,Stat. Ensemble,0.64,0.54,0.73,0.116,0.05,0.174
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+Sundial-Base,AutoETS,0.68,0.58,0.77,0.24,0.149,0.317
+Sundial-Base,AutoTheta,0.74,0.64,0.82,0.156,0.099,0.206
+Sundial-Base,Naive,0.82,0.74,0.89,0.315,0.248,0.38
+Sundial-Base,Seasonal Naive,0.88,0.81,0.94,0.273,0.225,0.319
+Sundial-Base,Drift,0.81,0.73,0.88,0.33,0.261,0.398
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+Stat. Ensemble,AutoETS,0.785,0.7,0.86,0.14,0.071,0.209
+Stat. Ensemble,AutoTheta,0.81,0.73,0.88,0.045,0.009,0.078
+Stat. Ensemble,Naive,0.84,0.77,0.91,0.224,0.169,0.277
+Stat. Ensemble,Seasonal Naive,0.835,0.77,0.9,0.177,0.138,0.221
+Stat. Ensemble,Drift,0.86,0.79,0.92,0.242,0.192,0.293
+AutoARIMA,TimesFM-2.5,0.09,0.03,0.15,-0.31,-0.401,-0.236
+AutoARIMA,TiRex,0.12,0.06,0.19,-0.305,-0.393,-0.233
+AutoARIMA,Toto-1.0,0.18,0.11,0.26,-0.266,-0.353,-0.187
+AutoARIMA,TabPFN-TS,0.17,0.1,0.24,-0.301,-0.395,-0.217
+AutoARIMA,Moirai-2.0,0.17,0.1,0.25,-0.251,-0.341,-0.173
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+AutoARIMA,Stat. Ensemble,0.35,0.26,0.44,-0.054,-0.095,-0.02
+AutoARIMA,AutoARIMA,0.5,0.5,0.5,0.0,0.0,0.0
+AutoARIMA,AutoETS,0.555,0.46,0.655,0.093,0.015,0.175
+AutoARIMA,AutoTheta,0.52,0.42,0.62,-0.006,-0.057,0.039
+AutoARIMA,Naive,0.72,0.63,0.8,0.183,0.118,0.242
+AutoARIMA,Seasonal Naive,0.8,0.735,0.875,0.133,0.09,0.178
+AutoARIMA,Drift,0.75,0.65,0.83,0.201,0.14,0.261
+AutoETS,TimesFM-2.5,0.15,0.09,0.22,-0.446,-0.605,-0.314
+AutoETS,TiRex,0.16,0.09,0.24,-0.44,-0.598,-0.303
+AutoETS,Toto-1.0,0.23,0.16,0.32,-0.396,-0.547,-0.263
+AutoETS,TabPFN-TS,0.23,0.15,0.31,-0.436,-0.617,-0.299
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+AutoETS,Chronos-Bolt,0.23,0.15,0.32,-0.362,-0.518,-0.235
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+AutoETS,AutoARIMA,0.445,0.345,0.54,-0.103,-0.212,-0.015
+AutoETS,AutoETS,0.5,0.5,0.5,0.0,0.0,0.0
+AutoETS,AutoTheta,0.47,0.37,0.56,-0.11,-0.214,-0.027
+AutoETS,Naive,0.62,0.53,0.72,0.098,0.018,0.177
+AutoETS,Seasonal Naive,0.685,0.585,0.775,0.043,-0.061,0.128
+AutoETS,Drift,0.74,0.66,0.82,0.119,0.042,0.194
+AutoTheta,TimesFM-2.5,0.08,0.03,0.14,-0.303,-0.383,-0.231
+AutoTheta,TiRex,0.07,0.02,0.12,-0.297,-0.374,-0.23
+AutoTheta,Toto-1.0,0.13,0.07,0.2,-0.258,-0.337,-0.188
+AutoTheta,TabPFN-TS,0.16,0.1,0.24,-0.294,-0.383,-0.22
+AutoTheta,Moirai-2.0,0.12,0.06,0.19,-0.243,-0.32,-0.172
+AutoTheta,Chronos-Bolt,0.08,0.03,0.13,-0.227,-0.302,-0.161
+AutoTheta,Sundial-Base,0.26,0.18,0.36,-0.185,-0.26,-0.11
+AutoTheta,Stat. Ensemble,0.19,0.12,0.27,-0.047,-0.085,-0.009
+AutoTheta,AutoARIMA,0.48,0.38,0.58,0.006,-0.041,0.054
+AutoTheta,AutoETS,0.53,0.44,0.63,0.099,0.026,0.177
+AutoTheta,AutoTheta,0.5,0.5,0.5,0.0,0.0,0.0
+AutoTheta,Naive,0.76,0.68,0.84,0.188,0.138,0.239
+AutoTheta,Seasonal Naive,0.78,0.69,0.86,0.138,0.099,0.18
+AutoTheta,Drift,0.78,0.7,0.86,0.206,0.158,0.26
+Naive,TimesFM-2.5,0.07,0.02,0.13,-0.603,-0.754,-0.476
+Naive,TiRex,0.07,0.02,0.12,-0.597,-0.75,-0.473
+Naive,Toto-1.0,0.09,0.04,0.15,-0.549,-0.689,-0.421
+Naive,TabPFN-TS,0.09,0.04,0.15,-0.592,-0.761,-0.461
+Naive,Moirai-2.0,0.13,0.07,0.2,-0.53,-0.674,-0.408
+Naive,Chronos-Bolt,0.11,0.05,0.17,-0.511,-0.649,-0.39
+Naive,Sundial-Base,0.18,0.11,0.26,-0.459,-0.613,-0.331
+Naive,Stat. Ensemble,0.16,0.09,0.23,-0.289,-0.383,-0.203
+Naive,AutoARIMA,0.28,0.2,0.37,-0.223,-0.319,-0.134
+Naive,AutoETS,0.38,0.28,0.47,-0.109,-0.215,-0.018
+Naive,AutoTheta,0.24,0.16,0.32,-0.231,-0.314,-0.161
+Naive,Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Naive,Seasonal Naive,0.475,0.39,0.565,-0.061,-0.146,0.017
+Naive,Drift,0.84,0.77,0.91,0.023,-0.005,0.048
+Seasonal Naive,TimesFM-2.5,0.05,0.01,0.1,-0.511,-0.633,-0.417
+Seasonal Naive,TiRex,0.05,0.01,0.1,-0.505,-0.623,-0.408
+Seasonal Naive,Toto-1.0,0.08,0.03,0.14,-0.46,-0.575,-0.362
+Seasonal Naive,TabPFN-TS,0.06,0.02,0.11,-0.501,-0.61,-0.409
+Seasonal Naive,Moirai-2.0,0.13,0.07,0.2,-0.442,-0.555,-0.347
+Seasonal Naive,Chronos-Bolt,0.09,0.04,0.15,-0.424,-0.527,-0.336
+Seasonal Naive,Sundial-Base,0.12,0.06,0.19,-0.375,-0.469,-0.29
+Seasonal Naive,Stat. Ensemble,0.165,0.1,0.23,-0.215,-0.283,-0.161
+Seasonal Naive,AutoARIMA,0.2,0.125,0.265,-0.153,-0.217,-0.099
+Seasonal Naive,AutoETS,0.315,0.225,0.415,-0.045,-0.147,0.057
+Seasonal Naive,AutoTheta,0.22,0.14,0.31,-0.16,-0.219,-0.11
+Seasonal Naive,Naive,0.525,0.435,0.61,0.058,-0.017,0.127
+Seasonal Naive,Seasonal Naive,0.5,0.5,0.5,0.0,0.0,0.0
+Seasonal Naive,Drift,0.64,0.53,0.73,0.079,-0.001,0.153
+Drift,TimesFM-2.5,0.08,0.03,0.14,-0.641,-0.804,-0.51
+Drift,TiRex,0.09,0.04,0.15,-0.635,-0.796,-0.504
+Drift,Toto-1.0,0.1,0.04,0.17,-0.585,-0.735,-0.459
+Drift,TabPFN-TS,0.11,0.05,0.17,-0.63,-0.807,-0.488
+Drift,Moirai-2.0,0.13,0.07,0.2,-0.566,-0.715,-0.435
+Drift,Chronos-Bolt,0.1,0.05,0.16,-0.546,-0.695,-0.42
+Drift,Sundial-Base,0.19,0.12,0.27,-0.493,-0.66,-0.354
+Drift,Stat. Ensemble,0.14,0.08,0.21,-0.32,-0.415,-0.238
+Drift,AutoARIMA,0.25,0.17,0.35,-0.252,-0.353,-0.163
+Drift,AutoETS,0.26,0.18,0.34,-0.135,-0.24,-0.044
+Drift,AutoTheta,0.22,0.14,0.3,-0.26,-0.351,-0.188
+Drift,Naive,0.16,0.09,0.23,-0.023,-0.051,0.005
+Drift,Seasonal Naive,0.36,0.27,0.47,-0.086,-0.181,0.001
+Drift,Drift,0.5,0.5,0.5,0.0,0.0,0.0

tables/pairwise_WQL.csv

@@ -0,0 +1,197 @@
+model_1,model_2,win_rate,win_rate_lower,win_rate_upper,skill_score,skill_score_lower,skill_score_upper
+TiRex,TiRex,0.5,0.5,0.5,0.0,0.0,0.0
+TiRex,TimesFM-2.5,0.535,0.435,0.63,-0.003,-0.021,0.015
+TiRex,Toto-1.0,0.685,0.6,0.77,0.03,0.009,0.05
+TiRex,TabPFN-TS,0.64,0.54,0.74,0.016,-0.033,0.063
+TiRex,Moirai-2.0,0.795,0.715,0.87,0.05,0.03,0.073
+TiRex,Chronos-Bolt,0.815,0.735,0.885,0.061,0.039,0.083
+TiRex,Sundial-Base,0.895,0.835,0.95,0.148,0.11,0.185
+TiRex,Stat. Ensemble,0.89,0.83,0.95,0.318,0.262,0.374
+TiRex,AutoARIMA,0.93,0.87,0.98,0.304,0.256,0.352
+TiRex,AutoETS,0.92,0.86,0.97,0.558,0.449,0.655
+TiRex,AutoTheta,0.95,0.9,0.99,0.421,0.365,0.479
+TiRex,Seasonal Naive,0.99,0.96,1.0,0.467,0.42,0.515
+TiRex,Naive,0.99,0.96,1.0,0.617,0.565,0.663
+TiRex,Drift,0.97,0.93,1.0,0.619,0.567,0.666
+TimesFM-2.5,TiRex,0.465,0.37,0.565,0.003,-0.016,0.021
+TimesFM-2.5,TimesFM-2.5,0.5,0.5,0.5,0.0,0.0,0.0
+TimesFM-2.5,Toto-1.0,0.57,0.485,0.67,0.033,0.007,0.059
+TimesFM-2.5,TabPFN-TS,0.64,0.54,0.74,0.018,-0.03,0.063
+TimesFM-2.5,Moirai-2.0,0.74,0.665,0.825,0.052,0.029,0.077
+TimesFM-2.5,Chronos-Bolt,0.77,0.695,0.845,0.064,0.042,0.086
+TimesFM-2.5,Sundial-Base,0.905,0.845,0.96,0.15,0.115,0.184
+TimesFM-2.5,Stat. Ensemble,0.9,0.84,0.95,0.32,0.263,0.377
+TimesFM-2.5,AutoARIMA,0.93,0.88,0.98,0.305,0.256,0.358
+TimesFM-2.5,AutoETS,0.9,0.84,0.95,0.558,0.449,0.655
+TimesFM-2.5,AutoTheta,0.96,0.91,0.99,0.423,0.364,0.483
+TimesFM-2.5,Seasonal Naive,0.99,0.97,1.0,0.468,0.422,0.519
+TimesFM-2.5,Naive,0.99,0.97,1.0,0.618,0.565,0.664
+TimesFM-2.5,Drift,0.97,0.93,1.0,0.62,0.567,0.668
+Toto-1.0,TiRex,0.315,0.23,0.4,-0.031,-0.053,-0.009
+Toto-1.0,TimesFM-2.5,0.43,0.33,0.515,-0.034,-0.063,-0.007
+Toto-1.0,Toto-1.0,0.5,0.5,0.5,0.0,0.0,0.0
+Toto-1.0,TabPFN-TS,0.54,0.45,0.63,-0.015,-0.073,0.036
+Toto-1.0,Moirai-2.0,0.59,0.49,0.68,0.02,-0.003,0.044
+Toto-1.0,Chronos-Bolt,0.61,0.52,0.695,0.032,0.003,0.061
+Toto-1.0,Sundial-Base,0.795,0.715,0.87,0.121,0.079,0.163
+Toto-1.0,Stat. Ensemble,0.81,0.73,0.88,0.297,0.235,0.356
+Toto-1.0,AutoARIMA,0.85,0.78,0.92,0.282,0.23,0.336
+Toto-1.0,AutoETS,0.84,0.76,0.91,0.545,0.433,0.645
+Toto-1.0,AutoTheta,0.93,0.87,0.97,0.403,0.341,0.465
+Toto-1.0,Seasonal Naive,0.96,0.92,0.99,0.45,0.397,0.501
+Toto-1.0,Naive,0.97,0.93,1.0,0.605,0.55,0.652
+Toto-1.0,Drift,0.97,0.93,1.0,0.607,0.551,0.656
+TabPFN-TS,TiRex,0.36,0.26,0.46,-0.016,-0.067,0.032
+TabPFN-TS,TimesFM-2.5,0.36,0.26,0.46,-0.018,-0.068,0.029
+TabPFN-TS,Toto-1.0,0.46,0.37,0.55,0.015,-0.037,0.068
+TabPFN-TS,TabPFN-TS,0.5,0.5,0.5,0.0,0.0,0.0
+TabPFN-TS,Moirai-2.0,0.55,0.46,0.65,0.035,-0.017,0.084
+TabPFN-TS,Chronos-Bolt,0.52,0.43,0.61,0.046,0.0,0.092
+TabPFN-TS,Sundial-Base,0.79,0.71,0.87,0.134,0.09,0.175
+TabPFN-TS,Stat. Ensemble,0.795,0.715,0.875,0.307,0.247,0.365
+TabPFN-TS,AutoARIMA,0.83,0.75,0.9,0.293,0.239,0.348
+TabPFN-TS,AutoETS,0.85,0.78,0.92,0.554,0.442,0.654
+TabPFN-TS,AutoTheta,0.94,0.89,0.98,0.412,0.352,0.471
+TabPFN-TS,Seasonal Naive,0.97,0.935,0.995,0.458,0.412,0.506
+TabPFN-TS,Naive,0.97,0.93,1.0,0.611,0.556,0.657
+TabPFN-TS,Drift,0.95,0.9,0.98,0.613,0.557,0.661
+Moirai-2.0,TiRex,0.205,0.13,0.285,-0.053,-0.078,-0.031
+Moirai-2.0,TimesFM-2.5,0.26,0.175,0.335,-0.055,-0.083,-0.029
+Moirai-2.0,Toto-1.0,0.41,0.32,0.51,-0.021,-0.046,0.003
+Moirai-2.0,TabPFN-TS,0.45,0.35,0.54,-0.036,-0.091,0.017
+Moirai-2.0,Moirai-2.0,0.5,0.5,0.5,0.0,0.0,0.0
+Moirai-2.0,Chronos-Bolt,0.59,0.505,0.67,0.012,-0.013,0.037
+Moirai-2.0,Sundial-Base,0.865,0.79,0.93,0.103,0.062,0.14
+Moirai-2.0,Stat. Ensemble,0.84,0.76,0.91,0.282,0.222,0.343
+Moirai-2.0,AutoARIMA,0.89,0.82,0.95,0.267,0.214,0.322
+Moirai-2.0,AutoETS,0.87,0.8,0.93,0.537,0.421,0.639
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tables/pivot_MASE.csv

@@ -0,0 +1,101 @@
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tables/pivot_MASE_baseline_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoTheta,AutoETS,Seasonal Naive,Naive,Drift
+ETT_15T,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+ETT_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ETT_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ETT_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_1H,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+LOOP_SEATTLE_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+M_DENSE_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+M_DENSE_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+SZ_TAXI_15T,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+SZ_TAXI_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+bizitobs_l2c_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+bizitobs_l2c_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1209,False,False,False,False,False,False,False,False,False,False,True,False,False,False
+boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1631,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+boomlet_1676,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+entsoe_15T,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_transactions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_transactions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+fred_md_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+fred_md_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hierarchical_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hierarchical_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hospital,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+jena_weather_10T,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+jena_weather_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+jena_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+kdd_cup_2022_10T,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+m5_1D,False,False,False,False,False,True,False,True,False,False,False,False,False,False
+m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+m5_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+proenfo_gfc12,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+proenfo_gfc14,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+proenfo_gfc17,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+redset_15T,False,False,False,False,False,False,False,True,True,False,True,False,False,False
+redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+redset_5T,False,False,False,False,False,False,False,False,False,False,True,False,False,False
+restaurant,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_sales_1D,False,False,False,False,False,True,False,False,False,False,False,False,False,False
+rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_with_weather_15T,False,False,False,False,False,False,False,True,True,False,False,False,False,False
+solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_MASE_leakage_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,Moirai-2.0,Chronos-Bolt,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoTheta,AutoETS,Seasonal Naive,Naive,Drift
+ETT_15T,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+ETT_1D,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+ETT_1H,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+ETT_1W,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_1D,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_1H,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_5T,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+M_DENSE_1D,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+M_DENSE_1H,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+SZ_TAXI_15T,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+SZ_TAXI_1H,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+bizitobs_l2c_1H,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+bizitobs_l2c_5T,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+boomlet_1062,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1209,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1225,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1230,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1282,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1487,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1631,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1676,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1855,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_1975,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_2187,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_285,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_619,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_772,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+boomlet_963,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ecdc_ili,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+entsoe_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+entsoe_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+entsoe_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_be,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_de,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_fr,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_np,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+epf_pjm,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+ercot_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_stores_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_stores_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_stores_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_transactions_1D,False,True,True,True,False,False,False,False,False,False,False,False,False,False
+favorita_transactions_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+favorita_transactions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+fred_md_2025/cee,False,True,True,True,False,False,False,False,False,False,False,False,False,False
+fred_md_2025/macro,False,True,True,True,False,False,False,False,False,False,False,False,False,False
+fred_qd_2025/cee,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+fred_qd_2025/macro,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hermes,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hierarchical_sales_1D,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+hierarchical_sales_1W,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+hospital,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+hospital_admissions_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+hospital_admissions_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+jena_weather_10T,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+jena_weather_1D,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+jena_weather_1H,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+kdd_cup_2022_10T,True,True,True,True,False,False,True,False,False,False,False,False,False,False
+kdd_cup_2022_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+kdd_cup_2022_30T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+m5_1D,False,True,True,True,False,False,False,False,False,False,False,False,False,False
+m5_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+m5_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+proenfo_gfc12,False,True,True,True,False,False,False,False,False,False,False,False,False,False
+proenfo_gfc14,False,True,True,True,False,False,False,False,False,False,False,False,False,False
+proenfo_gfc17,False,True,True,True,False,False,False,False,False,False,False,False,False,False
+redset_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+redset_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+restaurant,False,False,False,True,False,False,False,False,False,False,False,False,False,False
+rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_with_weather_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_SQL.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,Chronos-Bolt,Moirai-2.0,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
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tables/pivot_SQL_baseline_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,Chronos-Bolt,Moirai-2.0,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
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tables/pivot_SQL_leakage_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,Chronos-Bolt,Moirai-2.0,TabPFN-TS,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
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+world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WAPE.csv

@@ -0,0 +1,101 @@
+Task name,TimesFM-2.5,TiRex,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Naive,Seasonal Naive,Drift
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tables/pivot_WAPE_baseline_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TimesFM-2.5,TiRex,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Naive,Seasonal Naive,Drift
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+uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
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+us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
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+us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WAPE_leakage_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TimesFM-2.5,TiRex,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Naive,Seasonal Naive,Drift
+ETT_15T,False,False,False,False,True,False,False,False,False,False,False,False,False,False
+ETT_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False
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+ETT_1W,False,False,False,False,True,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False
+LOOP_SEATTLE_5T,False,False,False,False,True,False,False,False,False,False,False,False,False,False
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+australian_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+bizitobs_l2c_1H,False,False,False,False,True,False,False,False,False,False,False,False,False,False
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+gvar,False,False,False,False,False,False,False,False,False,False,False,False,False,False
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+hospital,False,False,False,False,True,False,False,False,False,False,False,False,False,False
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+restaurant,False,False,False,False,True,False,False,False,False,False,False,False,False,False
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+walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WQL.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
+ETT_15T,0.0914609728,0.0925315156,0.0951691601,0.0966499793,0.0933383153,0.0933383153,0.0969683943,0.1224205307,0.1224205307,0.1889663006,0.170235374,0.1224205307,0.2051287363,0.2108337564
+ETT_1D,0.2612366718,0.2678749413,0.2741924035,0.3179643477,0.2669855109,0.2669855109,0.3129149392,0.3227852613,0.3320927513,0.3649904644,0.360480833,0.3642035745,0.3538102937,0.3621092269
+ETT_1H,0.1923900276,0.1916090039,0.189981304,0.2034494101,0.2059476241,0.2059476241,0.2146782131,0.2620350459,0.2275419988,0.3554423083,0.387905148,0.2597090183,0.4903424451,0.5136644933
+ETT_1W,0.3834384084,0.3999054961,0.3901701003,0.4148319078,0.3737931634,0.3737931634,0.4643316617,0.4495446321,0.4761461158,0.4577441806,0.4986518854,0.4614224698,0.4614224698,0.4961494694
+LOOP_SEATTLE_1D,0.029926259,0.0292176506,0.0313094796,0.0295572487,0.0304002738,0.0304002738,0.0326217509,0.0310410872,0.0306907047,0.0311914855,0.0325479384,0.0397058571,0.0919262659,0.0978699313
+LOOP_SEATTLE_1H,0.0562291392,0.0529138924,0.0599067065,0.0580208445,0.0662523771,0.0662523771,0.0664487294,0.1307515175,0.1307515175,0.2388070951,0.2528752457,0.1307515175,0.3038598558,0.3206568541
+LOOP_SEATTLE_5T,0.0594513843,0.0643706408,0.0606719844,0.0689450513,0.0781392419,0.0781392419,0.0714930823,0.1151931596,0.1111745205,0.1270606325,0.121969972,0.081199984,0.1936404655,0.210202567
+M_DENSE_1D,0.0821914373,0.077372004,0.0906527349,0.0776412798,0.0817335467,0.0817335467,0.0844361527,0.1131422312,0.1051447846,0.1323504645,0.1459052349,0.1367572058,0.2323397334,0.2363624963
+M_DENSE_1H,0.1208851977,0.1143427338,0.1267713853,0.1296769427,0.1209575592,0.1209575592,0.1339606641,0.2346087824,0.2211301613,12.8518608164,0.7918015547,0.2731296131,0.84829413,0.8946267050000001
+SZ_TAXI_15T,0.1889248146,0.1894363506,0.1918294193,0.2040341404,0.197437778,0.197437778,0.2118998785,0.2672484294,0.2672484294,0.9991056751,0.2434722417,0.2672484294,0.6619538384,0.6804962505000001
+SZ_TAXI_1H,0.1368741884,0.1394923356,0.1399772683,0.1666049443,0.1447212932,0.1447212932,0.1764986928,0.2449234495,0.2187159408,4510502977.541462,0.3559208311,0.2910650923,0.9157567452,1.0097515601
+australian_tourism,0.0861105624,0.0761471858,0.1044141415,0.07310177,0.1053900171,0.1140447009,0.0836038879,0.0747720206,0.0866216851,0.0760110551,0.0823354804,0.0905870679,0.1922018184,0.2141353268
+bizitobs_l2c_1H,0.3395892762,0.3061232919,0.4180692521,0.3020415377,0.3259227696,0.3259227696,0.4028991374,1.2739406784,1.4044947577,1.4288888475,1.3542131559,2.0969368333,1.3379066541,1.3508241876
+bizitobs_l2c_5T,1.2724528657,0.7903189626,1.0846777557,0.6797159368,1.2750077013,1.2750077013,0.5857720953000001,1.5128526941,1.8771775481,1.6113720868,1.6069322946,2.2877704895,1.4507804453,1.731649134
+boomlet_1062,0.462540047,0.4710068786,0.4560339911,0.4854876351,0.4872201343,0.5409239931000001,0.5396810790000001,0.7822110511,0.5907259586,1.0658179181,1.085539266,0.7418110944,3.1996986673,3.4646472151
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tables/pivot_WQL_baseline_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
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+world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False

tables/pivot_WQL_leakage_imputed.csv

@@ -0,0 +1,101 @@
+Task name,TiRex,TimesFM-2.5,Toto-1.0,TabPFN-TS,Moirai-2.0,Chronos-Bolt,Sundial-Base,Stat. Ensemble,AutoARIMA,AutoETS,AutoTheta,Seasonal Naive,Naive,Drift
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+LOOP_SEATTLE_1D,False,False,False,False,True,False,False,False,False,False,False,False,False,False
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+redset_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+redset_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+redset_5T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+restaurant,False,False,False,False,True,False,False,False,False,False,False,False,False,False
+rohlik_orders_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_orders_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_sales_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rohlik_sales_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rossmann_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+rossmann_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_1W,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_with_weather_15T,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+solar_with_weather_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uci_air_quality_1D,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uci_air_quality_1H,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1D/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_nation_1W/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_utla_1D/new,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+uk_covid_utla_1W/cumulative,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1M,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1Q,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+us_consumption_1Y,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+walmart,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_co2_emissions,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_life_expectancy,False,False,False,False,False,False,False,False,False,False,False,False,False,False
+world_tourism,False,False,False,False,False,False,False,False,False,False,False,False,False,False