Summary

In this chapter, we covered auto-regressive models such as a moving average (MA) model to capture serial correlation using error relationship. On similar lines, auto-regressive (AR) models were covered, which set up the forecasting using the lags as dependent observations. The AR models are good to capture trend information. The ARMA-based approach was also illustrated, which integrates AR and MA models to capture any time-based trends and catastrophic events leading to a lot of error that will take time to correct such as an economy meltdown. All these models assume stationarity; in scenarios where stationarity is not present, a differencing-based model such as auto-regressive integrated moving average (ARIMA) is proposed, which performs differencing in time series datasets to remove any trend-related components. The forecasting approaches were illustrated with examples using Python's tsa module.

The current chapter focuses on using statistical methods for forecasting. The next chapter...