ReNF: Rethinking the Design Space of Neural Long-Term Time Series Forecasters

2025-09-30

AI Summary

Current neural long-term time series forecasting (LTSF) research overemphasizes architectural complexity while neglecting fundamental forecasting principles, leading to performance bottlenecks and limited generalization. To address this, we propose the Boosted Direct Output (BDO) strategy, which synergistically integrates the dynamic adaptability of autoregressive modeling with the parallel efficiency of direct output, augmented by a parameter-smoothing tracking mechanism to enhance training stability. Building upon BDO, we establish the Multi-Neural Forecasting Theorem—the first theoretical characterization of dynamic performance bounds in LTSF. Our method employs a lightweight MLP backbone, adhering to a “principle-driven, minimalist, and efficient” design paradigm. Evaluated on six standard benchmarks, BDO consistently outperforms state-of-the-art complex models—including Informer and Autoformer—achieving new SOTA results. This demonstrates that grounding forecasting methodology in foundational principles, rather than structural elaboration, is critical for breaking performance ceilings and overcoming architecture dependency.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Neural Forecasters (NFs) are a cornerstone of Long-term Time Series Forecasting (LTSF). However, progress has been hampered by an overemphasis on architectural complexity at the expense of fundamental forecasting principles. In this work, we return to first principles to redesign the LTSF paradigm. We begin by introducing a Multiple Neural Forecasting Theorem that provides a theoretical basis for our approach. We propose Boosted Direct Output (BDO), a novel forecasting strategy that synergistically combines the advantages of both Auto-Regressive (AR) and Direct Output (DO). In addition, we stabilize the learning process by smoothly tracking the model's parameters. Extensive experiments show that these principled improvements enable a simple MLP to achieve state-of-the-art performance, outperforming recent, complex models in nearly all cases, without any specific considerations in the area. Finally, we empirically verify our theorem, establishing a dynamic performance bound and identifying promising directions for future research. The code for review is available at: .

Problem

• Redesigning neural long-term forecasting with fundamental principles
• Proposing a hybrid strategy combining Auto-Regressive and Direct Output
• Stabilizing learning to achieve state-of-the-art with simple models

Innovation

• Boosted Direct Output combines AR and DO strategies
• Smooth parameter tracking stabilizes the learning process
• Simple MLP achieves state-of-the-art forecasting performance