Delete src

src/model_descriptions.py

@@ -1,522 +0,0 @@
-model_cards = dict(
-    nhitsm={
-        "Abstract": (
-            "The N-HiTS_M incorporates hierarchical interpolation and multi-rate data sampling "
-            "techniques. It assembles its predictions sequentially, selectively emphasizing "
-            "components with different frequencies and scales, while decomposing the input signal "
-            " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, "
-            "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural "
-            "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]"
-            "(https://arxiv.org/abs/2201.12886)"
-        ),
-        "Intended use": (
-            "The N-HiTS_M model specializes in monthly long-horizon forecasting by improving "
-            "accuracy and reducing the training time and memory requirements of the model."
-        ),
-        "Secondary use": (
-            "The interpretable predictions of the model produce a natural frequency time "
-            "series signal decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-HiTS_{M} to monthly data were it was pre-trained. "
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-HiTS_M was trained on 48,000 monthly series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@article{challu2022nhits,\n "
-            "author    = {Cristian Challu and \n"
-            "              Kin G. Olivares and \n"
-            "              Boris N. Oreshkin and \n"
-            "              Federico Garza and \n"
-            "              Max Mergenthaler and \n"
-            "              Artur Dubrawski}, \n "
-            "title     = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n "
-            "journal   = {Computing Research Repository},\n "
-            "volume    = {abs/2201.12886},\n "
-            "year      = {2022},\n "
-            "url       = {https://arxiv.org/abs/2201.12886},\n "
-            "eprinttype = {arXiv},\n "
-            "eprint    = {2201.12886},\n "
-            "biburl    = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}"
-        ),
-    },
-    nhitsh={
-        "Abstract": (
-            "The N-HiTS_{H} incorporates hierarchical interpolation and multi-rate data sampling "
-            "techniques. It assembles its predictions sequentially, selectively emphasizing "
-            "components with different frequencies and scales, while decomposing the input signal "
-            " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, "
-            "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural "
-            "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]"
-            "(https://arxiv.org/abs/2201.12886)"
-        ),
-        "Intended use": (
-            "The N-HiTS_{H} model specializes in hourly long-horizon forecasting by improving "
-            "accuracy and reducing the training time and memory requirements of the model."
-        ),
-        "Secondary use": (
-            "The interpretable predictions of the model produce a natural frequency time "
-            "series signal decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-HiTS_{H} to hourly data were it was pre-trained. "
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-HiTS_{H} was trained on 414 hourly series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@article{challu2022nhits,\n "
-            "author    = {Cristian Challu and \n"
-            "              Kin G. Olivares and \n"
-            "              Boris N. Oreshkin and \n"
-            "              Federico Garza and \n"
-            "              Max Mergenthaler and \n"
-            "              Artur Dubrawski}, \n "
-            "title     = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n "
-            "journal   = {Computing Research Repository},\n "
-            "volume    = {abs/2201.12886},\n "
-            "year      = {2022},\n "
-            "url       = {https://arxiv.org/abs/2201.12886},\n "
-            "eprinttype = {arXiv},\n "
-            "eprint    = {2201.12886},\n "
-            "biburl    = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}"
-        ),
-    },
-    nhitsd={
-        "Abstract": (
-            "The N-HiTS_D incorporates hierarchical interpolation and multi-rate data sampling "
-            "techniques. It assembles its predictions sequentially, selectively emphasizing "
-            "components with different frequencies and scales, while decomposing the input signal "
-            " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, "
-            "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural "
-            "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]"
-            "(https://arxiv.org/abs/2201.12886)"
-        ),
-        "Intended use": (
-            "The N-HiTS_D model specializes in daily long-horizon forecasting by improving "
-            "accuracy and reducing the training time and memory requirements of the model."
-        ),
-        "Secondary use": (
-            "The interpretable predictions of the model produce a natural frequency time "
-            "series signal decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-HiTS_D to daily data were it was pre-trained. "
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-HiTS_D was trained on 4,227 daily series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@article{challu2022nhits,\n "
-            "author    = {Cristian Challu and \n"
-            "              Kin G. Olivares and \n"
-            "              Boris N. Oreshkin and \n"
-            "              Federico Garza and \n"
-            "              Max Mergenthaler and \n"
-            "              Artur Dubrawski}, \n "
-            "title     = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n "
-            "journal   = {Computing Research Repository},\n "
-            "volume    = {abs/2201.12886},\n "
-            "year      = {2022},\n "
-            "url       = {https://arxiv.org/abs/2201.12886},\n "
-            "eprinttype = {arXiv},\n "
-            "eprint    = {2201.12886},\n "
-            "biburl    = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}"
-        ),
-    },
-    nhitsy={
-        "Abstract": (
-            "The N-HiTS_Y incorporates hierarchical interpolation and multi-rate data sampling "
-            "techniques. It assembles its predictions sequentially, selectively emphasizing "
-            "components with different frequencies and scales, while decomposing the input signal "
-            " and synthesizing the forecast [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, "
-            "Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. N-HiTS: Neural "
-            "Hierarchical Interpolation for Time Series Forecasting, Submitted working paper.]"
-            "(https://arxiv.org/abs/2201.12886)"
-        ),
-        "Intended use": (
-            "The N-HiTS_Y model specializes in yearly long-horizon forecasting by improving "
-            "accuracy and reducing the training time and memory requirements of the model."
-        ),
-        "Secondary use": (
-            "The interpretable predictions of the model produce a natural frequency time "
-            "series signal decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-HiTS_Y to yearly data were it was pre-trained. "
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-HiTS_{H} was trained on 23,000 yearly series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@article{challu2022nhits,\n "
-            "author    = {Cristian Challu and \n"
-            "              Kin G. Olivares and \n"
-            "              Boris N. Oreshkin and \n"
-            "              Federico Garza and \n"
-            "              Max Mergenthaler and \n"
-            "              Artur Dubrawski}, \n "
-            "title     = {N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting},\n "
-            "journal   = {Computing Research Repository},\n "
-            "volume    = {abs/2201.12886},\n "
-            "year      = {2022},\n "
-            "url       = {https://arxiv.org/abs/2201.12886},\n "
-            "eprinttype = {arXiv},\n "
-            "eprint    = {2201.12886},\n "
-            "biburl    = {https://dblp.org/rec/journals/corr/abs-2201-12886.bib}\n}"
-        ),
-    },
-    nbeatsm={
-        "Abstract": (
-            "The N-BEATS_M models is a model based on a deep stack multi-layer percentrons connected"
-            "with doubly residual connections. The model combines a multi-step forecasting strategy "
-            "with projections unto piecewise functions for its generic version or polynomials and "
-            "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas "
-            "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable "
-            "time series forecasting. 8th International Conference on Learning Representations, "
-            "ICLR 2020.](https://arxiv.org/abs/1905.10437)"
-        ),
-        "Intended use": (
-            "The N-BEATS_M is an efficient univariate forecasting model specialized in monthly "
-            "data, that uses the multi-step forecasting strategy."
-        ),
-        "Secondary use": (
-            "The interpretable variant of N-BEATSi_M produces a trend and seasonality "
-            "decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-BEATS_M to monthly data were it was pre-trained."
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-BEATS_M was trained on 48,000 monthly series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@inproceedings{oreshkin2020nbeats,\n "
-            "author    = {Boris N. Oreshkin and \n"
-            "              Dmitri Carpov and \n"
-            "              Nicolas Chapados and\n"
-            "              Yoshua Bengio},\n "
-            "title     = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n "
-            "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n "
-            "year      = {2020},\n "
-            "url       = {https://openreview.net/forum?id=r1ecqn4YwB}\n }"
-        ),
-    },
-    nbeatsh={
-        "Abstract": (
-            "The N-BEATS_H models is a model based on a deep stack multi-layer percentrons connected"
-            "with doubly residual connections. The model combines a multi-step forecasting strategy "
-            "with projections unto piecewise functions for its generic version or polynomials and "
-            "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas "
-            "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable "
-            "time series forecasting. 8th International Conference on Learning Representations, "
-            "ICLR 2020.](https://arxiv.org/abs/1905.10437)"
-        ),
-        "Intended use": (
-            "The N-BEATS_H is an efficient univariate forecasting model specialized in hourly "
-            "data, that uses the multi-step forecasting strategy."
-        ),
-        "Secondary use": (
-            "The interpretable variant of N-BEATSi_H produces a trend and seasonality "
-            "decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-BEATS_H to hourly data were it was pre-trained."
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-BEATS_H was trained on 414 hourly series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@inproceedings{oreshkin2020nbeats,\n "
-            "author    = {Boris N. Oreshkin and \n"
-            "              Dmitri Carpov and \n"
-            "              Nicolas Chapados and\n"
-            "              Yoshua Bengio},\n "
-            "title     = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n "
-            "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n "
-            "year      = {2020},\n "
-            "url       = {https://openreview.net/forum?id=r1ecqn4YwB}\n }"
-        ),
-    },
-    nbeatsd={
-        "Abstract": (
-            "The N-BEATS_D models is a model based on a deep stack multi-layer percentrons connected"
-            "with doubly residual connections. The model combines a multi-step forecasting strategy "
-            "with projections unto piecewise functions for its generic version or polynomials and "
-            "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas "
-            "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable "
-            "time series forecasting. 8th International Conference on Learning Representations, "
-            "ICLR 2020.](https://arxiv.org/abs/1905.10437)"
-        ),
-        "Intended use": (
-            "The N-BEATS_D is an efficient univariate forecasting model specialized in hourly "
-            "data, that uses the multi-step forecasting strategy."
-        ),
-        "Secondary use": (
-            "The interpretable variant of N-BEATSi_D produces a trend and seasonality "
-            "decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-BEATS_D to daily data were it was pre-trained."
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-BEATS_D was trained on 4,227 daily series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@inproceedings{oreshkin2020nbeats,\n "
-            "author    = {Boris N. Oreshkin and \n"
-            "              Dmitri Carpov and \n"
-            "              Nicolas Chapados and\n"
-            "              Yoshua Bengio},\n "
-            "title     = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n "
-            "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n "
-            "year      = {2020},\n "
-            "url       = {https://openreview.net/forum?id=r1ecqn4YwB}\n }"
-        ),
-    },
-    nbeatsw={
-        "Abstract": (
-            "The N-BEATS_W models is a model based on a deep stack multi-layer percentrons connected"
-            "with doubly residual connections. The model combines a multi-step forecasting strategy "
-            "with projections unto piecewise functions for its generic version or polynomials and "
-            "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas "
-            "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable "
-            "time series forecasting. 8th International Conference on Learning Representations, "
-            "ICLR 2020.](https://arxiv.org/abs/1905.10437)"
-        ),
-        "Intended use": (
-            "The N-BEATS_W is an efficient univariate forecasting model specialized in weekly "
-            "data, that uses the multi-step forecasting strategy."
-        ),
-        "Secondary use": (
-            "The interpretable variant of N-BEATSi_W produces a trend and seasonality "
-            "decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-BEATS_W to weekly data were it was pre-trained."
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-BEATS_W was trained on 359 weekly series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@inproceedings{oreshkin2020nbeats,\n "
-            "author    = {Boris N. Oreshkin and \n"
-            "              Dmitri Carpov and \n"
-            "              Nicolas Chapados and\n"
-            "              Yoshua Bengio},\n "
-            "title     = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n "
-            "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n "
-            "year      = {2020},\n "
-            "url       = {https://openreview.net/forum?id=r1ecqn4YwB}\n }"
-        ),
-    },
-    nbeatsy={
-        "Abstract": (
-            "The N-BEATS_Y models is a model based on a deep stack multi-layer percentrons connected"
-            "with doubly residual connections. The model combines a multi-step forecasting strategy "
-            "with projections unto piecewise functions for its generic version or polynomials and "
-            "harmonics for its interpretable version. [Boris N. Oreshkin, Dmitri Carpov, Nicolas "
-            "Chapados, Yoshua Bengio. N-BEATS: Neural basis expansion analysis for interpretable "
-            "time series forecasting. 8th International Conference on Learning Representations, "
-            "ICLR 2020.](https://arxiv.org/abs/1905.10437)"
-        ),
-        "Intended use": (
-            "The N-BEATS_Y is an efficient univariate forecasting model specialized in hourly "
-            "data, that uses the multi-step forecasting strategy."
-        ),
-        "Secondary use": (
-            "The interpretable variant of N-BEATSi_Y produces a trend and seasonality "
-            "decomposition."
-        ),
-        "Limitations": (
-            "The transferability across different frequencies has not yet been tested, it is "
-            "advisable to restrict the use of N-BEATS_Y to yearly data were it was pre-trained."
-            "This model purely autorregresive, transferability of models with exogenous variables "
-            "is yet to be done."
-        ),
-        "Training data": (
-            "N-BEATS_Y was trained on 23,000 yearly series from the M4 competition "
-            "[Spyros  Makridakis,  Evangelos  Spiliotis, and  Vassilios Assimakopoulos. The "
-            " M4  competition: 100,000  time  series and 61 forecasting methods. International "
-            "Journal of Forecasting, 36(1):54–74, 2020. ISSN  0169-2070.]"
-            "(https://www.sciencedirect.com/science/article/pii/S0169207019301128)"
-        ),
-        "Citation Info": (
-            "@inproceedings{oreshkin2020nbeats,\n "
-            "author    = {Boris N. Oreshkin and \n"
-            "              Dmitri Carpov and \n"
-            "              Nicolas Chapados and\n"
-            "              Yoshua Bengio},\n "
-            "title     = {{N-BEATS:} Neural basis expansion analysis for interpretable time series forecasting},\n "
-            "booktitle = {8th International Conference on Learning Representations, {ICLR} 2020},\n "
-            "year      = {2020},\n "
-            "url       = {https://openreview.net/forum?id=r1ecqn4YwB}\n }"
-        ),
-    },
-    arima={
-        "Abstract": (
-            "The AutoARIMA model is a classic autoregressive model that automatically explores ARIMA"
-            "models with a step-wise algorithm using Akaike Information Criterion. It applies to "
-            "seasonal and non-seasonal data and has a proven record in the M3 forecasting competition. "
-            "An efficient open-source version of the model was only available in R but is now also "
-            "available in Python. [StatsForecast: Lightning fast forecasting with statistical and "
-            "econometric models](https://github.com/Nixtla/statsforecast)."
-        ),
-        "Intended use": (
-            "The AutoARIMA is an univariate forecasting model, intended to produce automatic "
-            "predictions for large numbers of time series."
-        ),
-        "Secondary use": (
-            "It is a classical model and is an almost obligated forecasting baseline."
-        ),
-        "Limitations": (
-            "ARIMA model uses a recurrent prediction strategy. It concatenates errors on long "
-            "horizon forecasting settings. It is a fairly simple model that does not model "
-            "non-linear relationships."
-        ),
-        "Training data": (
-            "The AutoARIMA is a univariate model that uses only autorregresive data from "
-            "the target variable."
-        ),
-        "Citation Info": (
-            "@article{hyndman2008auto_arima,"
-            "title={Automatic Time Series Forecasting: The forecast Package for R},\n"
-            "author={Hyndman, Rob J. and Khandakar, Yeasmin},\n"
-            "volume={27},\n"
-            "url={https://www.jstatsoft.org/index.php/jss/article/view/v027i03},\n"
-            "doi={10.18637/jss.v027.i03},\n"
-            "number={3},\n"
-            "journal={Journal of Statistical Software},\n"
-            "year={2008},\n"
-            "pages={1–22}\n"
-            "}"
-        ),
-    },
-    exp_smoothing={
-        "Abstract": (
-            "Exponential smoothing is a classic technique using exponential window functions, "
-            "and one of the most successful forecasting methods. It has a long history, the "
-            "name was coined by Charles C. Holt. [Holt, Charles C. (1957). Forecasting Trends "
-            'and Seasonal by Exponentially Weighted Averages". Office of Naval Research '
-            "Memorandum.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134)."
-        ),
-        "Intended use": (
-            "Simple variants of exponential smoothing can serve as an efficient baseline method."
-        ),
-        "Secondary use": (
-            "The exponential smoothing method can also act as a low-pass filter removing "
-            "high-frequency noise. "
-        ),
-        "Limitations": (
-            "The method can face limitations if the series show strong discontinuities, or if "
-            "the high-frequency components are an important part of the predicted signal."
-        ),
-        "Training data": (
-            "Just like the ARIMA method, exponential smoothing uses only autorregresive data "
-            " from the target variable."
-        ),
-        "Citation Info": (
-            "@article{holt1957exponential_smoothing, \n"
-            "title = {Forecasting seasonals and trends by exponentially weighted moving averages},\n"
-            "author = {Charles C. Holt},\n"
-            "journal = {International Journal of Forecasting},\n"
-            "volume = {20},\n"
-            "number = {1},\n"
-            "pages = {5-10}\n,"
-            "year = {2004(1957)},\n"
-            "issn = {0169-2070},\n"
-            "doi = {https://doi.org/10.1016/j.ijforecast.2003.09.015},\n"
-            "url = {https://www.sciencedirect.com/science/article/pii/S0169207003001134},\n"
-            "}"
-        ),
-    },
-    prophet={
-        "Abstract": (
-            "Prophet is a widely used forecasting method. Prophet is a nonlinear regression model."
-        ),
-        "Intended use": ("Prophet can serve as a baseline method."),
-        "Secondary use": (
-            "The Prophet model is also useful for time series decomposition."
-        ),
-        "Limitations": (
-            "The method can face limitations if the series show strong discontinuities, or if "
-            "the high-frequency components are an important part of the predicted signal."
-        ),
-        "Training data": (
-            "Just like the ARIMA method and exponential smoothing, Prophet uses only autorregresive data "
-            " from the target variable."
-        ),
-        "Citation Info": (
-            "@article{doi:10.1080/00031305.2017.1380080,\n"
-            "author = {Sean J. Taylor and Benjamin Letham},\n"
-            "title = {Forecasting at Scale},\n"
-            "journal = {The American Statistician},\n"
-            "volume = {72},\n"
-            "number = {1},\n"
-            "pages = {37-45},\n"
-            "year  = {2018},\n"
-            "publisher = {Taylor & Francis},\n"
-            "doi = {10.1080/00031305.2017.1380080},\n"
-            "URL = {https://doi.org/10.1080/00031305.2017.1380080},\n"
-            "eprint = {https://doi.org/10.1080/00031305.2017.1380080},\n"
-            "}"
-        ),
-    },
-)

src/nf.py

@@ -1,188 +0,0 @@
-from itertools import chain
-from pathlib import Path
-from typing import List, Optional
-
-import neuralforecast as nf
-import numpy as np
-import pandas as pd
-import pytorch_lightning as pl
-from datasetsforecast.utils import download_file
-from hyperopt import hp
-from neuralforecast.core import NeuralForecast
-from neuralforecast.auto import NHITS as autoNHITS
-from neuralforecast.tsdataset import TimeSeriesDataset, TimeSeriesLoader
-from neuralforecast.models import NHITS
-import torch
-
-# GLOBAL PARAMETERS
-DEFAULT_HORIZON = 30
-HYPEROPT_STEPS = 10
-MAX_STEPS = 1000
-N_TS_VAL = 2 * 30
-
-MODELS = {
-    "Pretrained N-HiTS M4 Hourly": {
-        "card": "nhitsh",
-        "max_steps": 0,
-        "model": "nhits_m4_hourly",
-    },
-    "Pretrained N-HiTS M4 Hourly (Tiny)": {
-        "card": "nhitsh",
-        "max_steps": 0,
-        "model": "nhits_m4_hourly_tiny",
-    },
-    "Pretrained N-HiTS M4 Daily": {
-        "card": "nhitsd",
-        "max_steps": 0,
-        "model": "nhits_m4_daily",
-    },
-    "Pretrained N-HiTS M4 Monthly": {
-        "card": "nhitsm",
-        "max_steps": 0,
-        "model": "nhits_m4_monthly",
-    },
-    "Pretrained N-HiTS M4 Yearly": {
-        "card": "nhitsy",
-        "max_steps": 0,
-        "model": "nhits_m4_yearly",
-    },
-    "Pretrained N-BEATS M4 Hourly": {
-        "card": "nbeatsh",
-        "max_steps": 0,
-        "model": "nbeats_m4_hourly",
-    },
-    "Pretrained N-BEATS M4 Daily": {
-        "card": "nbeatsd",
-        "max_steps": 0,
-        "model": "nbeats_m4_daily",
-    },
-    "Pretrained N-BEATS M4 Weekly": {
-        "card": "nbeatsw",
-        "max_steps": 0,
-        "model": "nbeats_m4_weekly",
-    },
-    "Pretrained N-BEATS M4 Monthly": {
-        "card": "nbeatsm",
-        "max_steps": 0,
-        "model": "nbeats_m4_monthly",
-    },
-    "Pretrained N-BEATS M4 Yearly": {
-        "card": "nbeatsy",
-        "max_steps": 0,
-        "model": "nbeats_m4_yearly",
-    },
-}
-
-def download_models():
-    for _, meta in MODELS.items():
-        if not Path(f'./models/{meta["model"]}.ckpt').is_file():
-            download_file(
-                "./models/",
-                f'https://nixtla-public.s3.amazonaws.com/transfer/pretrained_models/{meta["model"]}.ckpt',
-            )
-
-download_models()
-
-class StandardScaler:
-    """This class helps to standardize a dataframe with multiple time series."""
-    def __init__(self):
-        self.norm: pd.DataFrame = None
-
-    def fit(self, X: pd.DataFrame) -> "StandardScaler":
-        self.norm = X.groupby("unique_id").agg({"y": [np.mean, np.std]})
-        self.norm = self.norm.droplevel(0, 1).reset_index()
-        return self
-
-    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
-        transformed = X.merge(self.norm, how="left", on=["unique_id"])
-        transformed["y"] = (transformed["y"] - transformed["mean"]) / transformed["std"]
-        return transformed[["unique_id", "ds", "y"]]
-
-    def inverse_transform(self, X: pd.DataFrame, cols: List[str]) -> pd.DataFrame:
-        transformed = X.merge(self.norm, how="left", on=["unique_id"])
-        for col in cols:
-            transformed[col] = (
-                transformed[col] * transformed["std"] + transformed["mean"]
-            )
-        return transformed[["unique_id", "ds"] + cols]
-
-def compute_ds_future(Y_df, fh):
-    if Y_df["unique_id"].nunique() == 1:
-        ds_ = pd.to_datetime(Y_df["ds"].values)
-        try:
-            freq = pd.infer_freq(ds_)
-        except:
-            freq = None
-        if freq is not None:
-            ds_future = pd.date_range(ds_[-1], periods=fh + 1, freq=freq)[1:]
-        else:
-            freq = ds_[-1] - ds_[-2]
-            ds_future = [ds_[-1] + (i + 1) * freq for i in range(fh)]
-        ds_future = list(map(str, ds_future))
-        return ds_future
-    else:
-        ds_future = chain(
-            *[compute_ds_future(df, fh) for _, df in Y_df.groupby("unique_id")]
-        )
-        return list(ds_future)
-
-def forecast_pretrained_model(Y_df: pd.DataFrame, model: str, fh: int, max_steps: int = 0):
-    if "unique_id" not in Y_df:
-        Y_df.insert(0, "unique_id", "ts_1")
-
-    scaler = StandardScaler()
-    scaler.fit(Y_df)
-    Y_df = scaler.transform(Y_df)
-
-    # Load the checkpoint and initialize NHITS with required parameters
-    file_ = f"./models/{model}.ckpt"
-    nhits = NeuralForecast.load_from_checkpoint(file_)
-
-    # Fit
-    if max_steps > 0:
-        train_dataset = TimeSeriesDataset.from_dataframe(Y_df, input_size=nhits.hparams.n_time_in, output_size=nhits.hparams.n_time_out)
-        train_loader = TimeSeriesLoader(dataset=train_dataset, batch_size=1, n_windows=32, shuffle=True)
-
-        trainer = pl.Trainer(
-            max_epochs=None,
-            checkpoint_callback=False,
-            logger=False,
-            max_steps=max_steps,
-            gradient_clip_val=1.0,
-            progress_bar_refresh_rate=1,
-            log_every_n_steps=1,
-        )
-
-        trainer.fit(nhits, train_loader)
-
-    # Forecast
-    forecast_df = nhits.forecast(Y_df=Y_df)
-    forecast_df = scaler.inverse_transform(forecast_df, cols=["y_5", "y_50", "y_95"])
-
-    n_ts = forecast_df["unique_id"].nunique()
-    if fh * n_ts > len(forecast_df):
-        forecast_df = (
-            forecast_df.groupby("unique_id")
-            .apply(lambda df: pd.concat([df] * fh).head(fh))
-            .reset_index(drop=True)
-        )
-    else:
-        forecast_df = forecast_df.groupby("unique_id").head(fh)
-    forecast_df["ds"] = compute_ds_future(Y_df, fh)
-
-    return forecast_df
-
-if __name__ == "__main__":
-    df = pd.read_csv(
-        "https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/ercot_COAST.csv"
-    )
-    df.columns = ["ds", "y"]
-    multi_df = pd.concat([df.assign(unique_id=f"ts{i}") for i in range(2)])
-    assert len(compute_ds_future(multi_df, 80)) == 2 * 80
-    for _, meta in MODELS.items():
-        # test just a time series (without unique_id)
-        forecast = forecast_pretrained_model(df, model=meta["model"], fh=80)
-        assert forecast.shape == (80, 5)
-        # test multiple time series
-        multi_forecast = forecast_pretrained_model(multi_df, model=meta["model"], fh=80)
-        assert multi_forecast.shape == (80 * 2, 5)

src/st_deploy.py

@@ -1,16 +0,0 @@
-import os
-import sys
-
-from streamlit.web import cli
-
-if __name__ == "__main__":
-    sys.argv = [
-        "streamlit",
-        "run",
-        f"{os.path.dirname(os.path.realpath(__file__))}/st_app.py",
-        "--server.port=8501",
-        "--server.address=0.0.0.0",
-        "--server.baseUrlPath=transfer-learning",
-        "--logger.level=debug",
-    ]
-    sys.exit(cli.main())