This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth,...

Time series monitoring methods, such as the Brown and Trigg methods, have the purpose of detecting pattern breaks (or “signals”) in time series data reliably and in a timely fashion. Traditionally, researchers have used the average run length statistic (ARL) on results from generated signal...

We have developed seven user defined functions (UDF) related to forecasting. The first four functions are for exponential smoothing. These are simple exponential smoothing, Holt’s fit, Winter’s method and Holt-Winter method. The next three functions can be used to calculate forecasting...

The vector innovations structural time series framework is proposed as a way of modelling a set of related time series. As with all multivariate approaches, the aim is to exploit potential interseries dependencies to improve the fit and forecasts. The model is based around an unobserved vector...

In earlier chapters we have considered only univariate models; we now proceed to examine multi-series extensions and to compare the multi-series innovations models with other multi-series schemes. We shall refer to our approach as the vector exponential smoothing (VES) framework. The innovations...