Research on event prediction in time-series data

Event prediction in time series is an important problem with many real world applications. Existing statistical and machine learning methods are not suitable for the problem. This paper describes a neural network system that predicts events by identifying features extracted from time-series data. A new feature extraction method is proposed and a corresponding clustering method is given. The method is applied to real time series and the resulting generalization performance of the trained feed-forward neural network predictors is analyzed. It shows that the method is effective in event prediction.