合成数据对时间序列预测效果因模型而异，混合通道的模型受益，独立通道的反而受损。
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
- 对比九组实验4218次运行，测试五种架构在七数据集上的合成数据增广效果。
- 在低资源场景下，时序混合模型（如TimesNet）用10%真实数据+合成数据可超越全量数据基线。
- 仅季节趋势生成器有效，硬性课程切换会恶化性能24%以上，需渐进式训练。
合成数据已彻底改变语言模型训练，但在时间序列预测中的作用仍不清晰。本文开展大规模实证研究：涵盖九个实验组、4218次运行，系统评估了五种模型架构、四种合成信号和七个数据集上的合成时间序列增广效果。结果表明，其影响具有强烈架构依赖性：通道混合模型（TimesNet、iTransformer）在多数试验中获益，而通道独立模型（DLinear、PatchTST）则持续退化。在特定低资源场景下，收益显著：仅使用10%天气数据并结合合成增广的TimesNet，在16组稀疏-数据组合中有4组超越全量数据基线。总体来看，增广在67%的试验中造成负面影响。进一步发现，仅季节趋势生成器在所有基准上可靠提升性能，而硬性课程切换会带来24%的均方误差恶化。这些结果为合成数据使用提供明确、可操作的指导：建议与通道混合模型配合使用，采用渐进式调度，且低资源增广需根据架构与数据集具体设计。代码开源于https://github.com/hugoiscracked/synthetic-ts/tree/main。
原文摘要 · Abstract (English)
Synthetic data has transformed language model training, yet its role in time series forecasting remains poorly understood. We present a large-scale empirical study: nine experiment groups, 4,218 runs systematically evaluating synthetic time series augmentation across five architectures, four synthetic signals and seven datasets. The effect is sharply architecture-conditional: channel-mixing models (TimesNet, iTransformer) benefit in the majority of trials, while channel-independent models (DLinear, PatchTST) are consistently degraded. In selected low-resource settings the gains are striking: TimesNet trained on only 10\% of Weather data with synthetic augmentation surpasses the full-data baseline (4 of 16 sparsity-dataset combinations). Averaged across all architectures, augmentation hurts in 67\% of trials. We further find that only the Seasonal-Trend generator reliably helps across the tested benchmarks, and that hard curriculum switching is actively harmful (+24\% MSE degradation). These results provide concrete, actionable guidelines on how to use synthetic data: use synthetic augmentation with channel-mixing architectures, use gradual annealing schedules, and treat low-resource augmentation as architecture- and dataset-dependent. Code is available at \href{https://github.com/hugoiscracked/synthetic-ts/tree/main}
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