We have already seen how time series models such as ARIMA can be used to make time series forecasts. While these models can prove to have high degrees of accuracy, they have one major shortcoming – they do not account for “shocks”, or sudden changes in a time series. Let’s see how we can potentially alleviate this problem using a model known as the Kalman Filter.
Continue reading “Kalman Filter: Modelling Time Series Shocks with KFAS in R”