Update app.py

app.py

@@ -1,10 +1,17 @@
 import gradio as gr
 import yfinance as yf
 from prophet import Prophet
-from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import LinearRegression, BayesianRidge
+from sklearn.svm import SVR
+from sklearn.preprocessing import MinMaxScaler
+from statsmodels.tsa.arima.model import ARIMA
+from xgboost import XGBRegressor
 import pandas as pd
+import numpy as np
 from datetime import datetime
 import plotly.graph_objects as go
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
 
 def download_data(ticker, start_date='2010-01-01'):
     """
@@ -45,14 +52,67 @@ def predict_future_prices(ticker, periods=1825):
     X_future = pd.to_numeric(pd.Series(range(len(data), len(data) + len(future_lr))))
     future_lr['yhat'] = model_lr.predict(X_future.values.reshape(-1, 1))
     
+    # ARIMA 모델 생성 및 학습
+    model_arima = ARIMA(data['y'], order=(1, 1, 1))
+    model_arima_fit = model_arima.fit()
+    forecast_arima = model_arima_fit.forecast(steps=periods)
+    future_arima = pd.DataFrame({'ds': future_dates, 'yhat': forecast_arima})
+    
+    # LSTM 모델 생성 및 학습
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data['y'].values.reshape(-1, 1))
+    X_train, y_train = [], []
+    for i in range(60, len(scaled_data)):
+        X_train.append(scaled_data[i-60:i, 0])
+        y_train.append(scaled_data[i, 0])
+    X_train, y_train = np.array(X_train), np.array(y_train)
+    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
+    
+    model_lstm = Sequential()
+    model_lstm.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
+    model_lstm.add(LSTM(units=50))
+    model_lstm.add(Dense(1))
+    model_lstm.compile(loss='mean_squared_error', optimizer='adam')
+    model_lstm.fit(X_train, y_train, epochs=10, batch_size=32)
+    
+    last_60_days = data['y'][-60:].values
+    scaled_last_60_days = scaler.transform(last_60_days.reshape(-1, 1))
+    X_test = []
+    X_test.append(scaled_last_60_days)
+    X_test = np.array(X_test)
+    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
+    pred_lstm = model_lstm.predict(X_test)
+    pred_lstm = scaler.inverse_transform(pred_lstm)
+    future_lstm = pd.DataFrame({'ds': future_dates[:periods], 'yhat': pred_lstm.flatten()})
+    
+    # XGBoost 모델 생성 및 학습
+    model_xgb = XGBRegressor(n_estimators=100, learning_rate=0.1)
+    model_xgb.fit(X.values.reshape(-1, 1), y)
+    future_xgb = pd.DataFrame({'ds': future_dates, 'yhat': model_xgb.predict(X_future.values.reshape(-1, 1))})
+    
+    # SVR 모델 생성 및 학습
+    model_svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
+    model_svr.fit(X.values.reshape(-1, 1), y)
+    future_svr = pd.DataFrame({'ds': future_dates, 'yhat': model_svr.predict(X_future.values.reshape(-1, 1))})
+    
+    # Bayesian Regression 모델 생성 및 학습
+    model_bayes = BayesianRidge()
+    model_bayes.fit(X.values.reshape(-1, 1), y)
+    future_bayes = pd.DataFrame({'ds': future_dates, 'yhat': model_bayes.predict(X_future.values.reshape(-1, 1))})
+    
     # 예측 결과 그래프 생성
     forecast_prophet['ds'] = forecast_prophet['ds'].dt.strftime('%Y-%m-%d')
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=forecast_prophet['ds'], y=forecast_prophet['yhat'], mode='lines', name='Prophet Forecast (Blue)'))
     fig.add_trace(go.Scatter(x=future_lr['ds'], y=future_lr['yhat'], mode='lines', name='Linear Regression Forecast (Red)', line=dict(color='red')))
+    fig.add_trace(go.Scatter(x=future_arima['ds'], y=future_arima['yhat'], mode='lines', name='ARIMA Forecast (Green)', line=dict(color='green')))
+    fig.add_trace(go.Scatter(x=future_lstm['ds'], y=future_lstm['yhat'], mode='lines', name='LSTM Forecast (Orange)', line=dict(color='orange')))
+    fig.add_trace(go.Scatter(x=future_xgb['ds'], y=future_xgb['yhat'], mode='lines', name='XGBoost Forecast (Purple)', line=dict(color='purple')))
+    fig.add_trace(go.Scatter(x=future_svr['ds'], y=future_svr['yhat'], mode='lines', name='SVR Forecast (Brown)', line=dict(color='brown')))
+    fig.add_trace(go.Scatter(x=future_bayes['ds'], y=future_bayes['yhat'], mode='lines', name='Bayesian Regression Forecast (Pink)', line=dict(color='pink')))
     fig.add_trace(go.Scatter(x=data['ds'], y=data['y'], mode='lines', name='Actual (Black)', line=dict(color='black')))
     
-    return fig, forecast_prophet[['ds', 'yhat', 'yhat_lower', 'yhat_upper']], future_lr[['ds', 'yhat']]
+    return fig, forecast_prophet[['ds', 'yhat', 'yhat_lower', 'yhat_upper']], future_lr[['ds', 'yhat']], future_arima[['ds', 'yhat']], future_lstm[['ds', 'yhat']], future_xgb[['ds', 'yhat']], future_svr[['ds', 'yhat']], future_bayes[['ds', 'yhat']]
 
 # Gradio 인터페이스 설정 및 실행
 with gr.Blocks() as app:
@@ -64,11 +124,16 @@ with gr.Blocks() as app:
     forecast_chart = gr.Plot(label="Forecast Chart")
     forecast_data_prophet = gr.Dataframe(label="Prophet Forecast Data")
     forecast_data_lr = gr.Dataframe(label="Linear Regression Forecast Data")
+    forecast_data_arima = gr.Dataframe(label="ARIMA Forecast Data")
+    forecast_data_lstm = gr.Dataframe(label="LSTM Forecast Data")
+    forecast_data_xgb = gr.Dataframe(label="XGBoost Forecast Data")
+    forecast_data_svr = gr.Dataframe(label="SVR Forecast Data")
+    forecast_data_bayes = gr.Dataframe(label="Bayesian Regression Forecast Data")
     
     forecast_button.click(
         fn=predict_future_prices,
         inputs=[ticker_input, periods_input],
-        outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr]
+        outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr, forecast_data_arima, forecast_data_lstm, forecast_data_xgb, forecast_data_svr, forecast_data_bayes]
     )
 
 app.launch()