Editorial Time series prediction competition The CATS benchmark.pdf

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Editorial Time series prediction competition The CATS benchmark.pdf

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Editorial Time series prediction competition The CATS benchmark

ARTICLE IN PRESS0925-2312/$ - se doi:10.1016/j.neNeurocomputing 70 (2007) 2325–2329 /locate/neucomEditorial Time series prediction competition: The CATS benchmark1. Introduction Time series forecasting is a challenge in many fields. In finance, one forecasts stock exchange courses or stock market indices; data processing specialists forecast the flow of information on their networks; producers of electricity forecast the load of the following day. The common point to their problems is the following: how can one analyze and use the past to predict the future? Many techniques exist: linear methods such as ARX, ARMA, etc. [1,7], and nonlinear ones such as artificial neural networks [2–5,9,11]. In general, these methods try to build a model of the process that is to be predicted. The model is then used on the last values of the series to predict future ones. The common difficulty to all methods is the determination of sufficient and necessary information for a good prediction. If the information is insufficient, the forecasting will be poor. On the contrary, if information is useless or redundant, modeling will be difficult or even skewed. In parallel with this determination, a prediction model has to be selected. In order to compare different prediction methods several competitions have been organized, for example: The Santa Fe Competition [11]; The K.U. Leuven Competition: Advanced Black-Box Techniques for Nonlinear Modeling: Theory and Applications [6]; The EUNITE competition [9]. After the competitions, their results have been published and the time series have become widely used benchmarks. The goal of these competitions is the prediction of the following values of a given time series (30–100 values to predict). Unfortunately, the long-term prediction of time series is a very difficult task, more difficult than the short-term prediction. Furthermore, after the publication of results, the real values that had to be predicted are also published. Thereafter it becomes