First published 10/3/2026, 9:14:15 PM · Last updated 10/3/2026, 9:14:15 PM

Beyond static frequency decomposition: Dynamic time frequency mixing for time series forecasting

Journal article that introduces a method called dynamic time frequency mixing for time series forecasting. Publisher listed as Elsevier BV and content type is a journal article.

Illustrative image: Map of carbon monoxide draped on globe: time Series from 8/1/2005 to 9/30/2005 from the Atmospheric Infrared Sounder AIRS on NASA Aqua satellite. — NASA/JPL/NASA Image and Video Library, NASA Media Usage Guidelines

Categories: science-and-space, technology

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 5/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 2/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 3/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 3/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The paper describes a new approach to time series forecasting that may be of interest to forecasting researchers and practitioners.

Sources

Beyond static frequency decomposition: Dynamic time frequency mixing for time series forecastingMetadata provided by Crossref. Usage terms

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