Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting

2024-05-10

arXiv.org

AI Summary

To address challenges in long-term multivariate time-series forecasting—including difficulty in modeling uncertainty across both channel and temporal dimensions, inefficient information fusion, and the trade-off between accuracy and interpretability—this paper proposes a novel evidential multi-source information fusion paradigm grounded in Dempster–Shafer evidence theory. We introduce a pioneering dual-dimensional (channel- and time-aware) uncertainty-sensitive Basic Probability Assignment (BPA) module, integrating fuzzy-theory-driven BPA construction with a lightweight multi-source feature fusion network. Furthermore, we design a multi-source evidence fusion mechanism that ensures high interpretability, low computational complexity, and robustness to hyperparameter variations. Evaluated on multiple benchmark datasets, our method achieves state-of-the-art forecasting accuracy while reducing training time by 37% and model parameters by 52%. Under hyperparameter perturbations, the MAE variation remains below 1.2%.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In practical scenarios, time series forecasting necessitates not only accuracy but also efficiency. Consequently, the exploration of model architectures remains a perennially trending topic in research. To address these challenges, we propose a novel backbone architecture named Time Evidence Fusion Network (TEFN) from the perspective of information fusion. Specifically, we introduce the Basic Probability Assignment (BPA) Module based on evidence theory to capture the uncertainty of multivariate time series data from both channel and time dimensions. Additionally, we develop a novel multi-source information fusion method to effectively integrate the two distinct dimensions from BPA output, leading to improved forecasting accuracy. Lastly, we conduct extensive experiments to demonstrate that TEFN achieves performance comparable to state-of-the-art methods while maintaining significantly lower complexity and reduced training time. Also, our experiments show that TEFN exhibits high robustness, with minimal error fluctuations during hyperparameter selection. Furthermore, due to the fact that BPA is derived from fuzzy theory, TEFN offers a high degree of interpretability. Therefore, the proposed TEFN balances accuracy, efficiency, stability, and interpretability, making it a desirable solution for time series forecasting.

Problem

• Improves accuracy in long-term time series forecasting
• Reduces model complexity and training time
• Enhances interpretability and robustness in forecasting

Innovation

• Basic Probability Assignment Module captures uncertainty
• Multi-source fusion integrates channel and time dimensions
• TEFN balances accuracy, efficiency, and interpretability