Cover Image

Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fan, Wei, Yi, Kun, Ye, Hangting, Ning, Zhiyuan, Zhang, Qi, An, Ning
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
by: Ye, Weiwei, et al.
Published: (2024)

Non-stationary Diffusion For Probabilistic Time Series Forecasting
by: Ye, Weiwei, et al.
Published: (2025)

Deep Coupling Network For Multivariate Time Series Forecasting
by: Yi, Kun, et al.
Published: (2024)

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
by: Li, Jinglin, et al.
Published: (2026)

FilterNet: Harnessing Frequency Filters for Time Series Forecasting
by: Yi, Kun, et al.
Published: (2024)

MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
by: Fan, Wei, et al.
Published: (2025)

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
by: Yi, Kun, et al.
Published: (2023)

Deep Learning for Time Series Forecasting: A Survey
by: Kong, Xiangjie, et al.
Published: (2025)

PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting
by: Zhou, Yingbo, et al.
Published: (2026)

Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting
by: Yang, Runze, et al.
Published: (2025)

Performative Time-Series Forecasting
by: Zhao, Zhiyuan, et al.
Published: (2023)

StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
by: Zhang, Jintao, et al.
Published: (2026)

Graph Deep Learning for Time Series Forecasting
by: Cini, Andrea, et al.
Published: (2023)

IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
by: Fan, Wei, et al.
Published: (2024)

RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
by: Lai, Chih-Yu, et al.
Published: (2025)

FreqLens: Interpretable Frequency Attribution for Time Series Forecasting
by: Chen, Chi-Sheng, et al.
Published: (2026)

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
by: Wang, Tan, et al.
Published: (2025)

Fredformer: Frequency Debiased Transformer for Time Series Forecasting
by: Piao, Xihao, et al.
Published: (2024)

Forecasting in Offline Reinforcement Learning for Non-stationary Environments
by: Ada, Suzan Ece, et al.
Published: (2025)

IdealTSF: Can Non-Ideal Data Contribute to Enhancing the Performance of Time Series Forecasting Models?
by: Wang, Hua, et al.
Published: (2025)

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting
by: Zhang, Fan, et al.
Published: (2026)

MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
by: Liu, Yiwen, et al.
Published: (2025)

TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting
by: Huang, Songtao, et al.
Published: (2025)

KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting
by: Ding, Kuiye, et al.
Published: (2025)

FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting
by: Li, Ruiqi, et al.
Published: (2024)

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting
by: Yue, Wenzhen, et al.
Published: (2025)

ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting
by: Yu, Guoqi, et al.
Published: (2025)

Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
by: Liu, Haoxin, et al.
Published: (2024)

Test Time Learning for Time Series Forecasting
by: Christou, Panayiotis, et al.
Published: (2024)

Multi-period Learning for Financial Time Series Forecasting
by: Zhang, Xu, et al.
Published: (2025)

LETS Forecast: Learning Embedology for Time Series Forecasting
by: Majeedi, Abrar, et al.
Published: (2025)

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
by: Teagan, Jonathan
Published: (2025)

Linear Attention is Enough in Spatial-Temporal Forecasting
by: Ning, Xinyu
Published: (2024)

Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
by: Zhou, Zhen, et al.
Published: (2026)

The Forecast After the Forecast: A Post-Processing Shift in Time Series
by: Liang, Daojun, et al.
Published: (2026)

BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting
by: Li, Zhixuan, et al.
Published: (2025)

Empowering Time Series Forecasting with LLM-Agents
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)

TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting
by: Hu, Yue, et al.
Published: (2026)

AltTS: A Dual-Path Framework with Alternating Optimization for Multivariate Time Series Forecasting
by: Yuan, Zhihang, et al.
Published: (2026)

TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)