A deep learning model to effectively capture mutation information in multivariate time series prediction

Abstract

In real-world complex multivariate time series data, mutation phenomena can significantly affect variation rules of target series. Meanwhile, there is no specific learning mechanism for the current deep learning model to capture mutation information in time series prediction. To this end, we propose a new deep learning model to capture mutation information between data. To capture the impact of mutation information on target series, a new function mapping is designed in the attention mechanism of the encoder to process the fusion of historical hidden state and cell state information; and an LSTM with transformation mechanism is proposed in the encoder to process the input information flow and learn the mutation information. In addition, an adaptive self-paced curriculum learning mechanism is designed to obtain mutation information that may be ignored among mini-batch samples. Finally, we define an objective function for multivariate time series prediction, which can extract the influence of temporal correlation information and mutation information within the data on target series. Our model can achieve superior performance than all baseline methods on five real-world datasets in different fields.