In the previous section, we identified non-stationarity in time series data and discussed techniques for making time series data stationary. With stationary data, we can proceed to perform statistical modeling such as prediction and forecasting. Prediction involves generating best estimates of in-sample data. Forecasting involves generating best estimates of out-of-sample data. Predicting future values is based on previously observed values. One such commonly used method is the Autoregressive Integrated Moving Average.
Forecasting and predicting a time series
About the Autoregressive Integrated Moving Average
The Autoregressive Integrated Moving Average (ARIMA) is a forecasting model for stationary time series based on linear...
