Wavelet-Temporal Neural Network for Multivariate Time Series Prediction

Multivariate time series prediction task has long been a crucial problem in machine learning since it includes applications in real life such as energy consumption, traffic congestion and economic trending. Challenge is that there are many factors affecting the accuracy of prediction, such as information in time domain and frequency domain, as well as interdependence among sequences. Therefore, effectively capturing and combining features in different domains becomes the focus of research. In this paper, a deep learning framework enhanced by wavelet transform is proposed to predict multivariate time series. Our model captures the long-term and short-term characteristics of time series in the time domain respectively through convolutional network. Further more, we use LSTM to achieve trending vector of short-term features, while for long-term features, we use the multi-head attention mechanism to get its representation. For frequency-domain features, our framework extracts from the scalo-gram generated by applying wavelet transform to the time sequence. Through the design of CNN convolution kernel, our model also ensures that the dependence between time series can be extracted. Experimental results show that our proposed model outperforms other baseline approaches on three of the four realworld datasets with sophisticated patterns.