SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
This addresses the problem of capturing inter-series dependencies in IoT and other applications for improved forecasting, representing an incremental advancement over existing Transformer-based models.
The paper tackles the challenge of long-term multivariate time series forecasting by introducing SageFormer, a series-aware framework that models inter-series dependencies using graph structures, achieving superior performance on real-world and synthetic datasets compared to state-of-the-art methods.
In the burgeoning ecosystem of Internet of Things, multivariate time series (MTS) data has become ubiquitous, highlighting the fundamental role of time series forecasting across numerous applications. The crucial challenge of long-term MTS forecasting requires adept models capable of capturing both intra- and inter-series dependencies. Recent advancements in deep learning, notably Transformers, have shown promise. However, many prevailing methods either marginalize inter-series dependencies or overlook them entirely. To bridge this gap, this paper introduces a novel series-aware framework, explicitly designed to emphasize the significance of such dependencies. At the heart of this framework lies our specific implementation: the SageFormer. As a Series-aware Graph-enhanced Transformer model, SageFormer proficiently discerns and models the intricate relationships between series using graph structures. Beyond capturing diverse temporal patterns, it also curtails redundant information across series. Notably, the series-aware framework seamlessly integrates with existing Transformer-based models, enriching their ability to comprehend inter-series relationships. Extensive experiments on real-world and synthetic datasets validate the superior performance of SageFormer against contemporary state-of-the-art approaches.