DFCNformer: A Transformer Framework for Non-Stationary Time-Series Forecasting Based on De-Stationary Fourier and Coefficient Network

DFCNformer: A Transformer Framework for Non-Stationary Time-Series Forecasting Based on De-Stationary Fourier and Coefficient Network

Time-series data are widely applied in real-world scenarios, but the non-stationary nature of their statistical properties and joint distributions over time poses challenges for existing forecasting models. To tackle this challenge, this paper introduces a forecasting model called DFCNformer (De-sta...

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Bibliographic Details
Main Authors: Yuxin Jin, Yuhan Mao, Genlang Chen
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Information
Subjects:
time-series prediction
non-stationary
attention mechanism
coefficient network
Transformer
Online Access:https://www.mdpi.com/2078-2489/16/1/62
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