Abstract: Time series data usually refer to a series of real value data with time interval, which widely exists in coal mine, finance, medical and other fields. In order to solve the problems of large amount of noise, low prediction accuracy and poor generalization performance in the existing time series data classification problems, a weighted ensemble classification method based on regularized extreme learning machine (RELM) is proposed. Firstly, aiming at the noise contained in time series data, the wavelet packet method is used to denoise time series data. Secondly, in view of the low prediction accuracy and poor generalization performance of time series data classification method, a weighted ensemble classification method based on RELM is proposed. By training the number of hidden layer nodes of RELM, RELM base classifier is effectively selected. Through PSO method, the weight of RELM based classifier is optimized. Finally, weighted ensemble classification is performed on the time series data. Experimental results show that the method can effectively classify time series data and improve the classification accuracy.

Key words: time series data, wavelet packet, regularized extreme learning machine, ensemble classification, weight optimization