Moving Average Process - Applied Time Series Analysis in Python and TensorFlow

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Let’s cover the moving average model.

The moving average model is used for forecasting. It uses the past forecast errors to predict the next point in time. We refer to the moving average model as the MA(q) model, where q is the order.

Here, we see a simulation of a moving average process of order 2. We will do the same in Python

Then, we plot the ACF function. What do you notice? It seems that after lag 2, the autocorrelation is not significant anymore.

Now you know that we can use the ACF plot to estimate the order q of a moving average model! After lag q, the autocorrelation should not be significant anymore. Alright, now let’s run some simulations in Python and see this for ourselves!

[piercelee]

There is an error in the video: the equation should be $y_t = Z_{t} + 0.9Z_{t-1} + 0.3Z_{t-2}$ .