Cover Image

Epistemic Error Decomposition for Multi-step Time Series Forecasting: Rethinking Bias-Variance in Recursive and Direct Strategies

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Green, Riku, Day, Huw, Abdallah, Zahraa S., Filho, Telmo M. Silva
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting
by: Green, Riku, et al.
Published: (2024)

Stratify: Unifying Multi-Step Forecasting Strategies
by: Green, Riku, et al.
Published: (2024)

Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models
by: Davies, Alex O., et al.
Published: (2023)

An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification
by: Serramazza, Davide Italo, et al.
Published: (2025)

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
by: Wang, Hao, et al.
Published: (2025)

LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
by: Pan, Sheng, et al.
Published: (2026)

DMamba: Decomposition-enhanced Mamba for Time Series Forecasting
by: Chen, Ruxuan, et al.
Published: (2026)

Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting
by: Nematirad, Reza, et al.
Published: (2025)

Ignore All Previous Instructions: Jailbreaking as a de-escalatory peace building practise to resist LLM social media bots
by: Day, Huw, et al.
Published: (2026)

A Decomposition Modeling Framework for Seasonal Time-Series Forecasting
by: Pang, Yining, et al.
Published: (2024)

ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting
by: Seo, Hyunwoo, et al.
Published: (2025)

Autocorrelation Reintroduces Spectral Bias in KANs for Time Series Forecasting
by: Zeng, Chen, et al.
Published: (2026)

LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
by: Delibasoglu, Ibrahim, et al.
Published: (2024)

OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting
by: Pan, Tingyue, et al.
Published: (2025)

Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
by: Li, Dongyuan, et al.
Published: (2026)

Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting
by: Chi, Jinjin, et al.
Published: (2026)

QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting
by: Aaraba, Abdallah, et al.
Published: (2024)

Rethinking Popularity Bias in Collaborative Filtering via Analytical Vector Decomposition
by: Liu, Lingfeng, et al.
Published: (2025)

Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition
by: Bumb, Mayank, et al.
Published: (2025)

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
by: Zhao, Lifan, et al.
Published: (2024)

A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting
by: He, Cheng, et al.
Published: (2025)

xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition
by: Stitsyuk, Artyom, et al.
Published: (2024)

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

KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators
by: Singh, Ashutosh, et al.
Published: (2024)

Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning
by: Bogahawatte, Jayanie, et al.
Published: (2024)

Causal Time-Series Synchronization for Multi-Dimensional Forecasting
by: Mayr, Michael, et al.
Published: (2024)

Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
by: Kim, Kai, et al.
Published: (2024)

DSAT-HD: Dual-Stream Adaptive Transformer with Hybrid Decomposition for Multivariate Time Series Forecasting
by: Wang, Zixu, et al.
Published: (2025)

Time Series Forecasting via Direct Per-Step Probability Distribution Modeling
by: Kong, Linghao, et al.
Published: (2025)

From Tokenizer Bias to Backbone Capability: A Controlled Study of LLMs for Time Series Forecasting
by: Zhang, Xinyu, et al.
Published: (2025)

Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
by: Luis, Carlos E., et al.
Published: (2023)

Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition
by: Mukherjee, Tanmoy, et al.
Published: (2026)

A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
by: Zhong, Shuhan, et al.
Published: (2023)

The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission
by: Spiro, Theodor
Published: (2026)

Skin Cancer Machine Learning Model Tone Bias
by: Pope, James, et al.
Published: (2024)

Numerion: A Multi-Hypercomplex Model for Time Series Forecasting
by: Cao, Hanzhong, et al.
Published: (2025)

ms-Mamba: Multi-scale Mamba for Time-Series Forecasting
by: Karadag, Yusuf Meric, et al.
Published: (2025)

Time Series Forecastability Measures
by: Wang, Rui, et al.
Published: (2025)

Ellipsoidal Time Series Forecasting
by: Wang, Qilin
Published: (2025)

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