A novel artificial neural network improves multivariate feature extraction in predicting correlated multivariate time series

The existing multivariate time series prediction schemes are inefficient in extracting intermediate features. This paper proposes an artificial neural network called Feature Path Efficient Multivariate Time Series Prediction (FPEMTSP) to predict the next element of the main time series in the presence of several secondary time series. We propose to generate all the possible combinations of the secondary time series and extract multivariate features by doing the Cartesian product of the main and the secondary time series features. Our calculations prove that the FPEMTSP's complexity and network size are acceptable. We have considered a few internal parameters in FPEMTSP that can be configured to improve the prediction accuracy and adjust the network size. We trained and evaluated FPEMTSP using two public datasets. Our evaluation revealed the optimal values for the internal parameters and showed that FPEMTSP surpasses the existing schemes in terms of prediction accuracy and the number of correctly predicted steps.