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ARXIV:2603.17436 · TIME SERIES FORECASTING · SUBMITTED 19 MAR · 21:58 UTC · FRESHNESS STALE
ARXIV:2603.17436TIME SERIES FORECASTINGSUBMITTED 19 MAR · 21:58 UTCFRESHNESS STALEarXiv
TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization.
Opportunity summary
Pain TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization. These variations lead to severe distribution shifts and consequently degrade predictive performance.
Non-stationarity is a fundamental challenge in multivariate long-term time series forecasting, often manifested as rapid changes in amplitude and phase. These variations lead to severe distribution shifts and consequently degrade predictive performance.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on seven real-world multivariate datasets demonstrate that TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons and outperforms state-of-the-art reversible normalization…
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization.
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10.48550/arXiv.2603.17436TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization.
Abstract
Non-stationarity is a fundamental challenge in multivariate long-term time series forecasting, often manifested as rapid changes in amplitude and phase. These variations lead to severe distribution shifts and consequently degrade predictive performance. Existing normalization-based methods primarily rely on first- and second-order statistics, implicitly assuming that distributions evolve smoothly and overlooking fine-grained temporal dynamics. To address these limitations, we propose TimeAPN, an Adaptive Amplitude-Phase Non-Stationarity Normalization framework that explicitly models and predicts non-stationary factors from both the time and frequency domains. Specifically, TimeAPN first models the mean sequence jointly in the time and frequency domains, and then forecasts its evolution over future horizons. Meanwhile, phase information is extracted in the frequency domain, and the phase discrepancy between the predicted and ground-truth future sequences is explicitly modeled to capture temporal misalignment. Furthermore, TimeAPN incorporates amplitude information into an adaptive normalization mechanism, enabling the model to effectively account for abrupt fluctuations in signal energy. The predicted non-stationary factors are subsequently integrated with the backbone forecasting outputs through a collaborative de-normalization process to reconstruct the final non-stationary time series. The proposed framework is model-agnostic and can be seamlessly integrated with various forecasting backbones. Extensive experiments on seven real-world multivariate datasets demonstrate that TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons and outperforms state-of-the-art reversible normalization methods.
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Dimensions overall score 7.0
PROBLEM
TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization. These variations lead to severe distribution shifts and consequently degrade predictive performance.
METHOD
Non-stationarity is a fundamental challenge in multivariate long-term time series forecasting, often manifested as rapid changes in amplitude and phase. These variations lead to severe distribution shifts and consequently degrade predictive performance.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on seven real-world multivariate datasets demonstrate that TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons and outperforms state-of-t...
WHY NOW
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization. These variations lead to severe distribution shifts and consequently degrade predictive performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Non-stationarity is a fundamental challenge in multivariate long-term time series forecasting, often manifested as rapid changes in amplitude and phase. These variations lead to severe distribution shifts and consequently degrade predictive performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on seven real-world multivariate datasets demonstrate that TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons and outperforms state-of-the-art reversible normalization methods. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization.
Segment
Time Series Forecasting
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