Future profiling of time series behavior

The study of time-dependent univariate systems plays an important role in several physical and applied sciences. Time-series behaviour of such systems is mostly complex in nature and sophisticated mathematical modelling tools are needed for making accurate forecasts. These forecasts can be used for specific purposes in different domains, for example, to plan resources, develop market strategies or control/understand complex systems. In the majority of successful applications of mathematical techniques used for forecasting, single point predictions are made. In this paper, a pattern recognition technique called Pattern Modelling and Recognition System (PMRS) is explored for making multiple forecasts into the future with four different time series. These multiple forecasts define a predicted behavioural profile of these univariate systems. These predicted profiles are compared against the actual behaviour of the studied systems on a number of error measures. The results show that the structural primitives used for multiple forecasts are a very promising method of profiling the true behaviour of univariate systems.