Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters

2026-05-07

AI Summary

This study systematically investigates the effectiveness of synthetic data in time series forecasting and its dependence on model architecture. Drawing on 4,218 experiments across nine configurations, the authors evaluate five prominent deep learning models—including TimesNet, iTransformer, DLinear, and PatchTST—on four synthetic signals and seven real-world datasets. The work reveals, for the first time, that the benefits of synthetic data are highly architecture-dependent: channel-mixing models consistently gain substantial improvements, particularly under low-data regimes, whereas synthetic data proves detrimental in 67% of all experimental settings. The study proposes effective usage strategies tailored to channel-mixing architectures and demonstrates that progressive scheduling outperforms hard curriculum switching. Among synthetic generation methods, only the seasonal-trend decomposition generator yields consistent performance gains.

Abstract

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}

Problem

• synthetic data
• time series forecasting
• deep learning
• data augmentation
• empirical study

Innovation

• synthetic data
• time series forecasting
• architecture-dependent augmentation
• channel-mixing models
• empirical evaluation