测试了时序大模型在含噪周期数据上的零样本预测能力
Evaluating Time Series Foundation Models on Noisy Periodic Time Series
- 用合成含噪周期数据评估多个时序大模型的零样本预测性能
- 高采样率下模型表现优于传统统计方法，但随噪声和周期增长迅速下降
- 适合关注时序大模型极限与实际应用边界的研究者阅读
尽管时序基础模型（TSFMs）在机器学习领域取得显著进展，但对其性能的严格评估仍严重不足。本文通过实证研究，评估多个主流时序基础模型在两个合成含噪周期时间序列数据集上的零样本、长时程预测能力。我们考察了不同噪声水平、基础频率和采样率下的模型表现，并以基于傅里叶变换（FFT）的方法和线性自回归（AR）模型作为基准。结果表明，在周期有界且采样率较高的情况下，时序基础模型可达到或超越统计方法；但随着周期变长、噪声增加、采样率降低以及时间序列形状复杂度提升，其预测能力显著下降。
原文摘要 · Abstract (English)
While recent advancements in foundation models have significantly impacted machine learning, rigorous tests on the performance of time series foundation models (TSFMs) remain largely underexplored. This paper presents an empirical study evaluating the zero-shot, long-horizon forecasting abilities of several leading TSFMs over two synthetic datasets constituting noisy periodic time series. We assess model efficacy across different noise levels, underlying frequencies, and sampling rates. As benchmarks for comparison, we choose two statistical techniques: a Fourier transform (FFT)-based approach and a linear autoregressive (AR) model. Our findings demonstrate that while for time series with bounded periods and higher sampling rates, TSFMs can match or outperform the statistical approaches, their forecasting abilities deteriorate with longer periods, higher noise levels, lower sampling rates and more complex shapes of the time series.
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