The Vector AutoRegression (VAR) family of models has been widely used for modelling and forecasting since the early 1980s. A VAR model is a conceptually simple system of multivariate models where each variable is explained by its own past values and the past values of all other variables in the system.
The Autoregressive Integrated Moving Average (ARIMA) family of models has now been a workhorse of time series forecasting for almost 50 years. The acronym indicates that the model is composed of different terms, an autoregressive term (AR), a moving-average term (MA), and an integration term (I) that accounts for the non-stationarity of the time series. The three terms combined constitute one of the most widely used discrete-time dynamic model.
Ex ante forecast is a forecast based solely on information available at the time of the forecast, whereas ex post forecast is a forecast that uses information beyond the time at which the forecast is made. Let’s discuss the two in more detail, as in different contexts the terms may mean slightly different things.
I was asked to illustrate how outliers can affect the standard sample correlation coefficient and show how the use of robust measures of correlation (association) could help when there is a need to automate the analysis. The post may be of interest to people with little background in statistics or data analysis.
A time ago the Western Power’s System Forecasting team, where I now belong in, initiated an upgrade of the software used for data analysis and forecasting. Several options were considered, including SPSS Modeller and a few industry specific options, but the final decision was to use SAS.