# SARIMA

Learn how to use SARIMA to make forecasts.

## We'll cover the following

## Understanding SARIMA

To review, SARIMA combines multiple time series components—seasonality, autoregression, integration, and moving average. The full model is defined by its order, which will represent the parameters for each of these components—

We might wonder how exactly to define the model order. There is no hard rule, but we can try a general framework that works well. For that, we need to address each component separately, starting with

### Finding $d$ and $D$

Since our series has trend and seasonality, it can't be stationary, so we need integration. Looking at the growth, the series seems to be closer to an exponential curve than to a straight line, so we can try differentiating twice by using `shift(1)`

twice and doing an

The `shift(n)`

method on a pandas series will move it `shift(1)`

twice in a row, we are taking the difference of the difference, thus reducing the effect of a nonlinear trend.

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