将时间序列预测拆解为三阶段模块化设计，提升精度与效率
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection
- 分三步走：表示、信息提取、投影，每步可独立优化
- 在7个数据集上达顶尖精度，参数量更低，训练推理更快
- 适合需要高效高精度预测的工程应用与研究者参考
随着Transformer的发展，时间序列预测取得显著进展，但仍面临有效序列表征、记忆构建和精确目标投影的挑战。每个数据集和预测配置构成独特任务，模型需克服特定难题才能实现准确预测。为此，本文将时间序列预测流程分解为三个核心阶段：输入序列表示、信息提取与记忆构建、最终目标投影。在各阶段中，评估多种架构配置，如用于特征提取的卷积层和用于信息提取的自注意力机制，在七个基准数据集上的实验表明，所提模型在保持先进预测精度的同时，大幅提升了计算效率，训练和推理时间减少，参数量降低。代码已开源于https://github.com/RobertLeppich/REP-Net。
原文摘要 · Abstract (English)
With the advent of Transformers, time series forecasting has seen significant advances, yet it remains challenging due to the need for effective sequence representation, memory construction, and accurate target projection. Time series forecasting remains a challenging task, demanding effective sequence representation, meaningful information extraction, and precise future projection. Each dataset and forecasting configuration constitutes a distinct task, each posing unique challenges the model must overcome to produce accurate predictions. To systematically address these task-specific difficulties, this work decomposes the time series forecasting pipeline into three core stages: input sequence representation, information extraction and memory construction, and final target projection. Within each stage, we investigate a range of architectural configurations to assess the effectiveness of various modules, such as convolutional layers for feature extraction and self-attention mechanisms for information extraction, across diverse forecasting tasks, including evaluations on seven benchmark datasets. Our models achieve state-of-the-art forecasting accuracy while greatly enhancing computational efficiency, with reduced training and inference times and a lower parameter count. The source code is available at https://github.com/RobertLeppich/REP-Net.
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