Improving long-term multivariate time series forecasting with a seasonal-trend decomposition-based 2-dimensional temporal convolution dense network

Abstract

Improving the accuracy of long-term multivariate time series forecasting is important for practical applications. Various Transformer-based solutions emerging for time series forecasting. Recently, some studies have verified that the most Transformer-based methods are outperformed by simple linear models in long-term multivariate time series forecasting. However, these methods have some limitations in exploring complex interdependencies among various subseries in multivariate time series. They also fall short in leveraging the temporal features of the data sequences effectively, such as seasonality and trends. In this study, we propose a novel seasonal-trend decomposition-based 2-dimensional temporal convolution dense network (STL-2DTCDN) to deal with these issues. We incorporate the seasonal-trend decomposition based on loess (STL) to explore the trend and seasonal features of the original data. Particularly, a 2-dimensional temporal convolution dense network (2DTCDN) is designed to capture complex interdependencies among various time series in multivariate time series. To evaluate our approach, we conduct experiments on six datasets. The results demonstrate that STL-2DTCDN outperforms existing methods in long-term multivariate time series forecasting.