A Robust Approach for Multivariate Time Series Forecasting

Abstract

Time series forecasting is often confronted with multivariate data, but few model is available in this situation. Besides, data distortion aggravates the difficulty to predict multivariate time series. To tackle such problems, we propose an approach based on convolutional neural network with a feature extraction layer added before convolution layer to extract multivariate features and handle multivariate time series data, as well as decreases the effect of distortion by transforming the sample into a denser representation with both its information and the information of its temporal neighbors. A full connection layer then fuses these extracted features and gets the final result. Given that events in the world are always related, using both the target time series and other related time series to forecast the future changes of the target dimension would achieve a better prediction. The proposed approach can process multivariate time series data and is robust to the number of samples, numeric ranges of data etc. Extensive experiments validate the effectiveness of the approach in accomplishing multivariate time series forecasting.