arima article draft

Author: steven-ja

content/posts/finance/stock_prediction/ARIMA/arima_example.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from statsmodels.tsa.arima.model import ARIMA
+from statsmodels.tsa.stattools import adfuller
+from sklearn.metrics import mean_squared_error
+import yfinance as yf
+
+# Download stock data
+ticker = "MSFT"
+start_date = "2018-01-01"
+end_date = "2023-06-23"
+data = yf.download(ticker, start=start_date, end=end_date)
+
+# Prepare the data
+ts = data['Close']
+
+# Check for stationarity
+def test_stationarity(timeseries):
+    result = adfuller(timeseries, autolag='AIC')
+    print('ADF Statistic:', result[0])
+    print('p-value:', result[1])
+
+test_stationarity(ts)
+
+# If non-stationary, difference the series
+ts_diff = ts.diff().dropna()
+test_stationarity(ts_diff)
+
+# Fit ARIMA model
+model = ARIMA(ts_diff, order=(1,1,1))
+results = model.fit()
+print(results.summary())
+
+# Forecast
+forecast = results.forecast(steps=30)
+help(forecast)
+
+# Plot the results
+plt.figure(figsize=(12,6))
+plt.plot(ts.index[-100:], ts.values[-100:], label='Observed')
+plt.plot(forecast.index, forecast.values, color='r', label='Forecast')
+plt.fill_between(forecast.index, 
+                 forecast.conf_int().iloc[:, 0], 
+                 forecast.conf_int().iloc[:, 1], 
+                 color='pink', alpha=0.3)
+plt.title(f'{ticker} Stock Price Prediction')
+plt.legend()
+plt.show()
+
+# Evaluate the model
+mse = mean_squared_error(ts.diff().dropna()[-30:], forecast)
+print(f'Mean Squared Error: {mse}')