Question

Explain how you would identify an appropriate ARIMA structure for a time series model.

Answer 1

ARIMA means  Auto Regressive Integrated Moving Average.

A time series is a sequence taken at successive equally spaced points in time.

ARMA model is a tool for understanding and predicting future values in this series.

There are different ARIMA structures for a time series model.

1. If the series has positive autocorrelations out to a high number of lags, then it probably needs a higher order of differencing.
2. If the lag-1 autocorrelation is zero or negative, or the autocorrelations are all small and patternless, then the series does not need a higher order of differencing. If the lag-1 autocorrelation is -0.5 or more negative, the series may be overdifferenced.
3. The optimal order of differencing is often the order of differencing at which the standard deviation is lowest.
4. A model with no orders of differencing assumes that the original series is stationary. A model with one order of differencing assumes that the original series has a constant average trend
5. A model with no orders of differencing normally includes a constant term