GTformer: Graph-Based Temporal-Order-Aware Transformer for Long-Term Series Forecasting

In the production environment of the Internet of Things (IoT), sensors of various qualities generate a large amount of multivariate time series (MTS) data. The long-term prediction of time series data generated by various IoT devices provides longer foresight and helps execute necessary resource scheduling or fault alarms in advance, thus improving the efficiency of system operation and ensuring system security. In recent years, deep learning models like Transformers have achieved advanced performance in multivariate long-term time series forecasting (MLTSF) tasks. However, many previous research attempts either overlooked the interseries dependencies or ignored the need to model the strict temporal order of MTS data. In this article, we introduce GTformer, a graph-based temporal-order-aware transformer model. We propose an adaptive graph learning method specifically designed for MTS data to capture both uni-directional and bi-directional relations. In addition, we generate positional encoding in a sequential way to emphasize the strict temporal order of time series. By adopting these two components, our model can have a better understanding of the interseries and intraseries dependencies of MTS data. We conducted extensive experiments on eight real-world data sets, and the results show that our model achieves better predictions compared with state-of-the-art methods.