Time series Forecasting with SARIMAX: A Guide to Predicting the Future Using Python
Predicting stock prices is a challenging yet exciting task in the field of data science. One of the most popular time series forecasting techniques is SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors) , which combines autoregressive models with seasonality and external factors. In this blog post, we’ll walk through how to use SARIMAX to forecast stock prices for multiple companies over the next 30 days using Python. Why SARIMAX for Stock Price Prediction? Stock prices often exhibit both trends and seasonality over time. SARIMAX is a powerful tool for modeling time series data, especially when there are seasonal components and external variables (like the day of the week) influencing the prices. This technique builds on the ARIMA model by adding two essential features: Seasonal Component : Captures the seasonal trends in the data (e.g., weekly cycles). Exogenous Variables (Exog) : Incorporates external variables like the day of the week, which m...