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[2602.17122] TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Summary
The paper presents TIFO, a Time-Invariant Frequency Operator designed to improve representation learning in nonstationary time series by addressing distribution shifts in frequency space.
Why It Matters
Understanding and mitigating distribution shifts in time series data is crucial for accurate forecasting. TIFO offers a novel approach that enhances model performance while reducing computational costs, making it relevant for researchers and practitioners in machine learning and data science.
Key Takeaways
- TIFO learns stationarity-aware weights across the frequency spectrum to improve time series forecasting.
- The method demonstrates significant performance improvements, achieving top results in various forecasting settings.
- TIFO reduces computational costs by 60%-70% compared to traditional methods, enhancing scalability.
Computer Science > Machine Learning arXiv:2602.17122 (cs) [Submitted on 19 Feb 2026] Title:TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series Authors:Xihao Piao, Zheng Chen, Lingwei Zhu, Yushun Dong, Yasuko Matsubara, Yasushi Sakurai View a PDF of the paper titled TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series, by Xihao Piao and 5 other authors View PDF HTML (experimental) Abstract:Nonstationary time series forecasting suffers from the distribution shift issue due to the different distributions that produce the training and test data. Existing methods attempt to alleviate the dependence by, e.g., removing low-order moments from each individual sample. These solutions fail to capture the underlying time-evolving structure across samples and do not model the complex time structure. In this paper, we aim to address the distribution shift in the frequency space by considering all possible time structures. To this end, we propose a Time-Invariant Frequency Operator (TIFO), which learns stationarity-aware weights over the frequency spectrum across the entire dataset. The weight representation highlights stationary frequency components while suppressing non-stationary ones, thereby mitigating the distribution shift issue in time series. To justify our method, we show that the Fourier transform of time series data implicitly induces eigen-decomposition in the frequency space. ...
