Single-Step Time Series Forecasting Based on Multilayer Attention and Recurrent Highway Networks

Multivariate time series forecasting plays an important role in many fields. However, due to the complex patterns of multivariate time series and the large amount of data, time series forecasting is still a challenging task. We propose a single-step forecasting method for time series based on multilayer attention and recurrent highway networks. Aiming at the shortcomings of the current traditional time series forecasting methods for extracting the temporal and spatial correlation characteristics of variables, as well as the two major problems existing in traditional recurrent neural networks, a single-step time series forecasting method is proposed to improving the accuracy of time series forecasting. This paper firstly defines the time series single-step forecast formally, then introduces the Attn-RHN (multilayer attention based recurrent highway networks) method in detail, and finally verifies the feasibility of our method on the corresponding data set.