Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting

2024-06-07

AI Summary

To address the performance bottleneck in long-term time series forecasting (LTSF) caused by insufficient modeling of intra-series temporal dependency (TDT), this paper proposes TDAlign—a plug-and-play temporal dependency alignment framework. Its core contributions are threefold: (1) a novel delta-alignment loss function that explicitly enforces consistency between predicted and ground-truth sequences in terms of dynamic trend evolution; (2) a parameter-free, adaptive multi-objective loss balancing strategy that introduces no additional learnable parameters; and (3) a lightweight alignment module with linear time complexity O(L) and constant space complexity O(1). Evaluated on seven real-world datasets, TDAlign consistently improves forecasting accuracy, reducing overall prediction error by 1.47%–9.19% and delta error—measuring trend fidelity—by 4.57%–15.78%, thereby demonstrating both effectiveness and computational efficiency.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by extbf{1.47%} to extbf{9.19%} and change value errors by extbf{4.57%} to extbf{15.78%}, highlighting its substantial performance improvements.

Problem

• Inadequate modeling of temporal dependencies within target time series
• Performance bottleneck in long-term time series forecasting methods
• Lack of consistent variation pattern learning in predictions

Innovation

• Aligns prediction change values with target patterns
• Uses adaptive loss balancing without extra parameters
• Plug-and-play framework with linear time complexity