Skill Up: Exploring SARIMAX
In the ever-evolving world of data science, few tools offer the predictive power and flexibility of SARIMAX. Whether you're forecasting sales, modeling seasonal trends, or untangling the complexities of time series data, SARIMAX (Seasonal Auto Regressive Integrated Moving Average with Exogenous Regressors) is a powerhouse worth exploring. In this post, we’ll dive into how SARIMAX works, why it’s so effective, and how you can start using it in Python to unlock deeper insights from your data. If you're ready to level up your forecasting game and learn something truly impactful, you're in the right place.
First the terms. SARIMAX stands for:
Seasonality
Seasonality refers to patterns that repeat at regular intervals over time—like monthly sales spikes, yearly temperature changes, or weekly website traffic. SARIMAX can model these recurring fluctuations to improve forecast accuracy.
AutoRegressive (AR)
The autoregressive part means the model uses past values of the time series to predict future ones. For example, if sales were high last month, they might influence this month’s sales. The AR component captures this dependency.
Integrated (I)
Integration deals with making a time series stationary—meaning its statistical properties like mean and variance don’t change over time. If your data shows trends, the model “integrates” it by differencing (subtracting previous values) to stabilize it.
Moving Average (MA)
The moving average part uses past forecast errors to improve future predictions. If the model consistently underestimates or overestimates, MA helps correct that by learning from those mistakes.
Exogenous Regressors (X)
These are external variables that can influence your time series. For instance, if you're forecasting electricity demand, temperature could be an exogenous regressor. SARIMAX allows you to include these outside influences to make your model smarter and more context-aware.
When do you use it?
You’d use SARIMAX when you're working with time series data that shows seasonal patterns, is influenced by external factors, and requires a flexible, accurate forecasting model. It’s especially useful when your data has repeating cycles—like monthly sales or yearly climate trends—and when outside variables (like marketing spend or temperature) impact your results. SARIMAX combines autoregressive elements (using past values), moving averages (correcting past errors), and integration (handling trends) to make the data more predictable. This makes it ideal for applications like financial forecasting, demand planning, and environmental modeling where both internal trends and external influences matter.
It’s ideal for:
Financial forecasting
Inventory and demand planning
Weather prediction
Any scenario where both internal trends and external influences matter
If you want to get deep down into the formulas and try it for yourself, check out this article: https://www.geeksforgeeks.org/python/complete-guide-to-sarimax-in-python/
Have fun exploring, grab some seasonal data and get coding!
Post conceived of by Justeen Gales and written with the support of Microsoft Copilot