Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import yfinance as yf
+from TSEnsemble.ensemble import Ensemble
+from TSEnsemble import arima, nn, utils
+
+# Function to load stock data using yfinance
+def get_stock_data(symbol, start_date, end_date):
+    stock_data = yf.download(symbol, start=start_date, end=end_date)
+    return stock_data['Close']
+
+# Load stock data
+symbol = 'AAPL'  # Replace with the desired stock symbol
+start_date = '2020-01-01'
+end_date = '2023-01-01'
+stock_prices = get_stock_data(symbol, start_date, end_date)
+
+# Set up ARIMA, CNN, LSTM, and Transformer models
+ar = arima.auto_arima(stock_prices, method='stepwise', season=12, max_p=3, max_q=3, max_Q=3, max_P=3, train_split=0.8, plot=False)
+
+transformer = nn.generate_transformer(
+    look_back=12,
+    horizon=1,
+    n_features=1,
+    num_transformer_blocks=4,
+    dropout=0.25,
+    head_size=256,
+    num_heads=4,
+    ff_dim=4,
+    mlp_units=[128],
+    mlp_dropout=0.4
+)
+
+lstm = nn.generate_rnn(look_back=12, hidden_layers=1, units=64, type="LSTM", dropout=0.0)
+
+cnn = nn.generate_cnn(look_back=12, hidden_layers=3, kernel_size=2, filters=64, dilation_rate=1, dilation_mode="multiplicative")
+
+# Create an ensemble model
+ensemble_model = Ensemble(models=[ar, cnn, lstm, transformer], regressor='wmean')
+
+# Fit the ensemble model
+ensemble_model.fit(stock_prices, train_size=0.8, look_back=12, val_size=0.2, train_models_size=0.7, epochs=20, batch_size=16, metric="rmse")
+
+# Forecast with the ensemble model
+ensemble_forecast = ensemble_model.forecast(stock_prices, steps=12, fig_size=(10, 6))
+
+# Streamlit app
+st.title("Stock Price Prediction App")
+
+# Display historical stock prices
+st.subheader("Historical Stock Prices")
+st.line_chart(stock_prices)
+
+# Display ensemble forecast
+st.subheader("Ensemble Forecast")
+st.line_chart(ensemble_forecast)
+
+# Display ARIMA forecast
+arima_forecast = utils.model_forecast(ar, stock_prices, steps=12)
+st.subheader("ARIMA Forecast")
+st.line_chart(arima_forecast)