Oct 101 min read

What is the difference between autoregressive (AR) and moving average (MA) models?

In simple terms, here’s the difference between Autoregressive (AR) and Moving Average (MA) models:

1. Autoregressive (AR) Model:

Imagine you’re trying to predict the weather for tomorrow.
In an AR model, you would look at the weather of the past few days (say, the last 3 or 5 days) to make a prediction.
The idea is that today’s weather depends on how the weather has been recently. So, it’s like saying, “The weather today is similar to what it was yesterday, the day before, and so on.”
The more past days you look at, the more "history" you’re using to predict the future.

Now imagine you’re still predicting the weather, but instead of looking at the actual weather from previous days, you look at how wrong your previous forecasts were (the errors in your predictions).
In an MA model, you’re saying, “Let me adjust today’s prediction based on how off I was in predicting the weather on previous days.”
It’s like learning from your past mistakes. If you were way off yesterday, you’ll adjust today’s prediction accordingly.

AR model: You predict based on how things have been in the past (using actual past values).
MA model: You predict based on how wrong your previous guesses were (using past errors).

Both models help make better predictions, but they rely on different ways of looking at the past!