Combinatorial time series forecasting based on clustering algorithms and neural networks

Abstract

Time series analysis utilising more than a single forecasting approach is a procedure originated many years ago as an attempt to improve the performance of the individual model forecasts. In the literature there is a wide range of different approaches but their success depends on the forecasting performance of the individual schemes. A clustering algorithm is often employed to distinguish smaller sets of data that share common properties. The application of clustering algorithms in combinatorial forecasting is discussed with an emphasis placed on the formulation of the problem so that better forecasts are generated. Additionally, the hybrid clustering algorithm that assigns data depending on their distance from the hyper-plane that provides their optimal modelling is applied. The developed cluster-based combinatorial forecasting schemes were examined in a single-step ahead prediction of the pound-dollar daily exchange rate and demonstrated an improvement over conventional linear and neural based combinatorial schemes.