ReNF: Rethinking the Design Space of Neural Long-Term Time Series Forecasters
This work addresses inefficiencies in neural long-term time series forecasting for applications requiring accurate predictions, though it appears incremental by refining existing strategies.
The paper tackles the problem of long-term time series forecasting by redesigning the neural forecasting paradigm based on fundamental principles, resulting in a simple MLP achieving state-of-the-art performance that outperforms complex models in nearly all cases.
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: .