analiza aiqbench
This paper introduces FreDF, a novel approach to time series forecasting that addresses estimation bias caused by ignoring autocorrelation in future labels. By shifting the learning objective into the frequency domain, the method significantly outperforms existing state-of-the-art models while remaining compatible with various architectures. The work represents a meaningful advancement in handling temporal dependencies within standard machine learning frameworks for prediction tasks.
FreDF: Learning to Forecast in the Frequency Domain
arXiv:2402.02399v3 Announce Type: replace Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation within historical data, the correlations among future labels are often overlooked. Specifically, modern forecasting models primarily adhere to the Direct Forecast (DF) paradigm, generating multi-step forecasts independently and disregarding label autocorrelation over time. In this work, we demonstrate that the learning objective of DF is biased in the presence of label autocorrelation. To address this issue, we propose the Frequency-enhanced Direct Forecast (FreDF), which mitigates label autocorrelation by learning to forecast in the frequency domain, thereby reducing estimation bias. Our experiments show that FreDF significantly outperforms existing state-of-the-art methods and is compatible with a variety of forecast models. Code is available at https://github.com/Master-PLC/FreDF.
Abstract:Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation within historical data, the correlations among future labels are often overlooked. Specifically, modern forecasting models primarily adhere to the Direct Forecast (DF) paradigm, generating multi-step forecasts independently and disregarding label autocorrelation over time. In this work, we demonstrate that the learning objective of DF is biased in the presence of label autocorrelation. To address this issue, we propose the Frequency-enhanced Direct Forecast (FreDF), which mitigates label autocorrelation by learning to forecast in the frequency domain, thereby reducing estimation bias. Our experiments show that FreDF significantly outperforms existing state-of-the-art methods and is compatible with a variety of forecast models. Code is available at this https URL.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Applications (stat.AP); Machine Learning (stat.ML)
arXiv:2402.02399 [cs.LG]
(or arXiv:2402.02399v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2402.02399 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 227 cuvinte
Subiecte conexe
msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
arXiv · 27 aug. 2026
Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting
arXiv · 26 sept. 2026
RouteTS: Frequency-Time Routing for Time Series Forecasting
arXiv · 19 aug. 2026
SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting
arXiv · 15 iun. 2026