In the previous section, Time series analysis, we explored the basics behind time series. To perform correct predictions of future events based on what happened in the past, it is necessary to construct an appropriate numerical simulation model.
Choosing an appropriate model is extremely important as it reflects the underlying structure of the series. In practice, two types of models are available: linear or nonlinear. These can be selected based on whether the current value of the series is a linear or nonlinear function of past observations.
The following are the most widely used models for forecasting time series data:
- AR (autoregressive)
- MA (moving average)
- ARMA (autoregressive moving average)
- ARIMA (autoregressive integrated moving average)
