How do I choose ARIMA parameters for my time series data?

How do I choose ARIMA parameters for my time series data?

Choosing ARIMA parameters involves selecting the order of the autoregressive (p), differencing (d), and moving average (q) components. You can start by analyzing the autocorrelation and partial autocorrelation plots.

What are autocorrelation and partial autocorrelation plots?

Autocorrelation plots show the correlation of a time series with its own past values, while partial autocorrelation plots show the correlation after removing the effects of earlier lags.

How do I interpret these plots?

In the autocorrelation plot, look for significant spikes to determine the value of p. In the partial autocorrelation plot, the point where the spikes drop off indicates the value of q.

What if my data is non-stationary?

If your data is non-stationary, you may need to difference the data (set d) until it becomes stationary. You can also use tests like the Augmented Dickey-Fuller test to check for stationarity.