A sparse kernel algorithm for online time series data prediction

► A new sparsification method based on the significance measure of the training sample is proposed. ► The algorithm is updated by recursive least square algorithm in an online fashion. ► Sparsification and system updating are unified in a same framework. ► Deterministic convergence in kernel weight error vector is guaranteed. ► The computational cost is low and it is suitable for real-time applications.