Non-Stationary Time Series Forecasting

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Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
Jan 30, 2026Yannis Montreuil, Letian Yu, Axel Carlier +2Cost-Sensitive Learning Adaptive Model Routing
School of Computing National University of Singapore · Université de Toulouse Fédération ENAC ISAE-SUPAERO ONERA · Institute for Infocomm Research A*STAR, Singapore
KAN vs LSTM Performance in Time Series Forecasting
Nov 23, 2025Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova +1Time Series Forecasting Non-Stationary Time Series Forecasting
Department of Mathematics, Swinburne University of Technology, Hawthorn, 3122, Victoria, Australia.
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
Apr 2, 2025Xiaobei Zou, Luolin Xiong, Kexuan Zhang +2Non-Stationary Time Series Forecasting Dynamic Graph Learning
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China · Faculty of Informatics, Università della Svizzera italiana, 69000 Lugano, Switzerland · Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy

Jan 30, 2026, cs.LGJ/K move · Enter open · S save

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models

Yannis Montreuil, Letian Yu, Axel Carlier, Lai Xing Ng+1

School of Computing National University of Singapore · Université de Toulouse Fédération ENAC ISAE-SUPAERO ONERA · Institute for Infocomm Research A*STAR, Singapore

Cost-Sensitive Learning Adaptive Model Routing Learning to Defer Decision-Focused Learning Non-Stationary Time Series Forecasting State Space Models

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Learning-to-defer (L2D) lets a predictor decide, at each round, whether to issue its own forecast or pay for an expert's. In non-stationary time series this decision must keep adapting, although deployment reveals only the consulted expert's forecast while a historical archive records every expert with the target. L2D-SLDS learns from this archive a switching state-space model of the target and all expert forecasts, whose shared and expert-specific states describe how experts move together and apart. Its predictive law supplies the internal forecast and the expected cost of every consultation, which a greedy router minimizes, and one consultation also updates the beliefs about unconsulted and unavailable experts. We prove sublinear regret against a changing conditional-risk oracle without exploration, when the candidate models are accurate and either the archive separates them or live feedback reveals cost differences. On three real datasets, L2D-SLDS has the lowest cost among eight bandit routers and adapts its consultation rate to the fee.

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Latest papers 63

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models

KAN vs LSTM Performance in Time Series Forecasting

DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting