Update app.py

app.py

@@ -3,9 +3,13 @@ import pandas as pd
 import numpy as np
 from statsmodels.tsa.arima.model import ARIMA
 from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
+from sklearn.preprocessing import MinMaxScaler
 import plotly.graph_objects as go
+from tensorflow import keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
 
-def forecast_stock(days_ahead):
+def forecast_arima(days_ahead):
     df = pd.read_excel("Microsoft_stock_data.xlsx")
     df['Date'] = pd.to_datetime(df['Date'])
     df = df.sort_values('Date')
@@ -15,26 +19,136 @@ def forecast_stock(days_ahead):
     model = ARIMA(data, order=(1,1,1))
     fitted = model.fit()
     
- 
     forecast = fitted.forecast(steps=int(days_ahead))
     
- 
     fig = go.Figure()
-    fig.add_trace(go.Scatter(x=df['Date'].tail(100), y=data[-100:], name='Historical'))
+    fig.add_trace(go.Scatter(x=df['Date'].tail(100), y=data[-100:], name='Historical', line=dict(color='blue')))
     future_dates = pd.date_range(start=df['Date'].iloc[-1], periods=int(days_ahead)+1)[1:]
-    fig.add_trace(go.Scatter(x=future_dates, y=forecast, name='Forecast'))
-    fig.update_layout(title='Stock Price Forecast', xaxis_title='Date', yaxis_title='Price')
+    fig.add_trace(go.Scatter(x=future_dates, y=forecast, name='ARIMA Forecast', line=dict(color='red')))
+    fig.update_layout(title='ARIMA Stock Price Forecast', xaxis_title='Date', yaxis_title='Price')
+    
+    return fig
+
+def forecast_lstm(days_ahead):
+    df = pd.read_excel("Microsoft_stock_data.xlsx")
+    df['Date'] = pd.to_datetime(df['Date'])
+    df = df.sort_values('Date')
+    
+    data = df['Close'].values.reshape(-1, 1)
+    
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data)
+    
+    lookback = 60
+    X_train, y_train = [], []
+    for i in range(lookback, len(scaled_data)):
+        X_train.append(scaled_data[i-lookback: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 = Sequential([
+        LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)),
+        Dropout(0.2),
+        LSTM(units=50, return_sequences=False),
+        Dropout(0.2),
+        Dense(units=25),
+        Dense(units=1)
+    ])
+    
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    model.fit(X_train, y_train, batch_size=32, epochs=10, verbose=0)
+    
+    last_sequence = scaled_data[-lookback:]
+    forecast = []
+    
+    for _ in range(int(days_ahead)):
+        prediction = model.predict(last_sequence.reshape(1, lookback, 1), verbose=0)
+        forecast.append(prediction[0, 0])
+        last_sequence = np.append(last_sequence[1:], prediction)
+    
+    forecast = scaler.inverse_transform(np.array(forecast).reshape(-1, 1))
+    
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'].tail(100), y=data[-100:, 0], name='Historical', line=dict(color='blue')))
+    future_dates = pd.date_range(start=df['Date'].iloc[-1], periods=int(days_ahead)+1)[1:]
+    fig.add_trace(go.Scatter(x=future_dates, y=forecast.flatten(), name='LSTM Forecast', line=dict(color='green')))
+    fig.update_layout(title='LSTM Stock Price Forecast', xaxis_title='Date', yaxis_title='Price')
+    
+    return fig
+
+def forecast_comparison(days_ahead):
+    df = pd.read_excel("Microsoft_stock_data.xlsx")
+    df['Date'] = pd.to_datetime(df['Date'])
+    df = df.sort_values('Date')
+    
+    data = df['Close'].values
+    
+    arima_model = ARIMA(data, order=(1,1,1))
+    arima_fitted = arima_model.fit()
+    arima_forecast = arima_fitted.forecast(steps=int(days_ahead))
+    
+    scaled_data = MinMaxScaler(feature_range=(0, 1)).fit_transform(data.reshape(-1, 1))
+    lookback = 60
+    X_train, y_train = [], []
+    for i in range(lookback, len(scaled_data)):
+        X_train.append(scaled_data[i-lookback: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))
+    
+    lstm_model = Sequential([
+        LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)),
+        Dropout(0.2),
+        LSTM(units=50, return_sequences=False),
+        Dropout(0.2),
+        Dense(units=25),
+        Dense(units=1)
+    ])
+    
+    lstm_model.compile(optimizer='adam', loss='mean_squared_error')
+    lstm_model.fit(X_train, y_train, batch_size=32, epochs=10, verbose=0)
+    
+    scaler = MinMaxScaler(feature_range=(0, 1)).fit(data.reshape(-1, 1))
+    last_sequence = scaled_data[-lookback:]
+    lstm_forecast = []
+    
+    for _ in range(int(days_ahead)):
+        prediction = lstm_model.predict(last_sequence.reshape(1, lookback, 1), verbose=0)
+        lstm_forecast.append(prediction[0, 0])
+        last_sequence = np.append(last_sequence[1:], prediction)
+    
+    lstm_forecast = scaler.inverse_transform(np.array(lstm_forecast).reshape(-1, 1)).flatten()
+    
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df['Date'].tail(100), y=data[-100:], name='Historical', line=dict(color='blue')))
+    future_dates = pd.date_range(start=df['Date'].iloc[-1], periods=int(days_ahead)+1)[1:]
+    fig.add_trace(go.Scatter(x=future_dates, y=arima_forecast, name='ARIMA Forecast', line=dict(color='red', dash='dash')))
+    fig.add_trace(go.Scatter(x=future_dates, y=lstm_forecast, name='LSTM Forecast', line=dict(color='green', dash='dot')))
+    fig.update_layout(title='ARIMA vs LSTM: Stock Price Forecast Comparison', xaxis_title='Date', yaxis_title='Price')
     
     return fig
 
 with gr.Blocks() as demo:
     gr.Markdown("# 📈 Time Series Forecasting: ARIMA vs LSTM")
-    gr.Markdown("**Microsoft Stock Price Forecasting** - This app demonstrates ARIMA model forecasting.")
+    gr.Markdown("**Microsoft Stock Price Forecasting** - Compare ARIMA and LSTM models.")
     
     days = gr.Slider(1, 90, value=30, label="Days to Forecast")
-    plot = gr.Plot()
     
-    demo.load(forecast_stock, days, plot)
-    days.change(forecast_stock, days, plot)
+    with gr.Tabs():
+        with gr.Tab("ARIMA Model"):
+            arima_plot = gr.Plot()
+            days.change(forecast_arima, days, arima_plot)
+            demo.load(forecast_arima, days, arima_plot)
+        
+        with gr.Tab("LSTM Model"):
+            lstm_plot = gr.Plot()
+            days.change(forecast_lstm, days, lstm_plot)
+        
+        with gr.Tab("Comparison"):
+            comparison_plot = gr.Plot()
+            days.change(forecast_comparison, days, comparison_plot)
 
 demo.launch()