Time Series Forecasting with Covariates

Latest papers

35 papers14 papers in the last four weeks, up 250% on the four weeks before. 0.1% of all new papers.

EnergyBench: a live, gate-aligned benchmark of day-ahead forecasts for European power markets
Nov 5, 2026Evgenii Genov, Ander Martinez Alonso, Christoph BergmeirForecasting Benchmarks Electricity Price Forecasting
EVERGi Research Group, Flanders Alliance for Climate Technology (FACT), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium · DTU Wind and Energy Systems, Technical University of Denmark · Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain +1
Thermally Conditioned Time-Series Forecasting of Grain Growth in Polycrystalline Materials
Nov 4, 2026Eliane Younes, Elie Hachem, Marc BernackiMaterials Science Time Series Forecasting
Mines Paris, PSL University Centre for material forming (CEMEF), UMR CNRS 06904 Sophia Antipolis, France
ElecLoad: Adaptive Electricity Load Forecasting Guided by Multiple Events
Nov 4, 2026Huajian Zhang, Mingyue Cheng, Xingpeng Gao +4Electricity Load Forecasting Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Short-Term Forecasting of Heterogeneous Wind Power Fleets: Global Temporal Fusion Transformer Leveraging Static Covariates
Nov 4, 2026Viktor Walter, Andreas WagnerWind Power Forecasting Time Series Forecasting
Faculty of Management Science and Engineering, Karlsruhe University of Applied Sciences, Karlsruhe, Germany · Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
Learning From Static Context in Federated Time Series Forecasting
Nov 4, 2026Viktor Walter, Andreas Wagner, Fabian StrickerTime Series Forecasting Federated Learning Aggregation
Faculty of Management Science and Engineering, Karlsruhe University of Applied Sciences (HKA), Karlsruhe, Germany · Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany · Institute of Data-Centric Software Systems (IDSS), Karlsruhe University of Applied Sciences (HKA), Karlsruhe, Germany
TSFM Meets LLM: Context as Covariates
Nov 3, 2026Hemanth Ram, Sai Shankar Narasimhan, Shubhankar Agarwal +1Time Series Forecasting LLM-Based Time Series Forecasting
The University of Texas at Austin, Austin, TX, 78712.
CATF: A Manager-Worker Framework for Context-Aware Time-Series Forecasting
Nov 3, 2026Yueqiao Chen, Dongyu LiuContext-Aware Time Series Forecasting Time Series Forecasting
UC Davis, Davis, CA, USA
Chronos-2: A foundation model for multivariate time series forecasting
Oct 30, 2026Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken +22Multivariate Time Series Forecasting Time-Series Foundation Models
Amazon Web Services · University of Freiburg · Amazon +3
FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Forecasting
Oct 29, 2026Chenghao Gu, Zhuokang Shen, Shenghao Wu +3Retail Demand Forecasting Time Series Forecasting
East China Normal University, Shanghai, China · China Merchants Bank Credit Card Center, Shanghai, China
FACTS-MAS: A Multi-Agent Architecture for Adaptive Forecasting from Heterogeneous Evidence
Oct 28, 2026Madumita Karthikeyan, Umang Rajnikant Bid, Rong PanTime Series Forecasting Agentic Time Series Forecasting
Timestral
Oct 27, 2026Adrien Petralia, Alexandre Gavaudan, Artjom Joosen +353Time Series Forecasting Time-Series Foundation Models
Model-agnostic predictability quantification of multivariable time series
Oct 23, 2026Yuxuan Shu, Vasileios LamposMultivariate Time Series Forecasting Time Series Forecasting with Covariates
Centre for Artificial Intelligence, Department of Computer Science, University College London
Forecasting Associations, Not Interventions: A Preregistered Causal Audit of Covariate-Aware Time Series Foundation Models
Oct 22, 2026Zong-Han BaiTime-Series Foundation Models Causal Effect Estimation
National Tsing Hua University, Hsinchu, Taiwan.
Data-driven and physics-guided hindcasting of measured wave spectra
Oct 21, 2026Sander RikkaTime Series Forecasting Physics-Informed ML
Department of Marine Systems, Tallinn University of Technology
Asymmetric Bidirectional Predictive Dynamics in Google Search and COVID-19 Time Series: A Multi-Horizon Forecast-Error Analysis
Oct 20, 2026Andrei Velichko, Imen MahmoudTime Series Forecasting Epidemic Forecasting
Institute of Physics and Technology, Petrozavodsk State University, 33 Lenina Prospekt, Petrozavodsk 185910, Republic of Karelia, Russia · University of Mannouba, Tunisia · Laboratory of Research in Innovative and Management, Risk, Accounting and Finance
WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting
Oct 19, 2026Arjun Ashok, Mihir Parmar, Palash Goyal +6Time Series Forecasting Time Series Forecasting with Covariates
Google · Mila - Quebec AI Institute · Université de Montréal +1
Multi-site Learning for Forecasting Day-Ahead Wind-Power Production for Onshore Wind Farms in Great Britain with Deep Neural Networks
Oct 18, 2026James Kean, Aidan O'SullivanWind Power Forecasting Time Series Forecasting
UCL Energy Institute, University College London
Data-driven forecasting of tropical cloud forest phenology: A dynamical systems and machine learning hybrid approach
Oct 18, 2026Pedro Garcia, Saul FloresTime Series Forecasting Time Series Forecasting with Covariates
Departamento de Física, Facultad de Ingeniería, Universidad Católica Andrés Bello, Caracas, Venezuela · Laboratorio de Ecología de Suelos, Centro de Ecología, Instituto Venezolano de Investigaciones Científicas, Caracas, Venezuela
World Models, Trees and Foundation Models for Retail Forecasting: Evidence from Four Public Data Sets
Oct 18, 2026Karthik Iyer, Madan DabbeeruRetail Demand Forecasting Time Series Forecasting
ASE: Attribution-Guided Semantic Evolution for Time Series Forecasting
Oct 16, 2026Yuxuan Zheng, Zhaoyang Li, Purui Liu +2Time Series Forecasting Time Series Forecasting with Covariates
Peking University
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models
Oct 9, 2026Alexander Kreusel, Maximilian Beichter, Alexandra Nikoltchovska +4Time Series Forecasting with Covariates Time Series Forecasting
Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe, Germany
JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
Sep 29, 2026Donguk Kwon, Wooseok Jeong, Dongha LeeTime Series Forecasting Time Series Forecasting with Covariates
Yonsei University · Konkuk University
XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching
Sep 28, 2026Ziyang Zhang, Hanyin Cheng, Xiangfei Qiu +3Multivariate Time Series Forecasting Time Series Forecasting
East China Normal University
WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting
Sep 25, 2026Yuhan Zhu, Xiangfei Qiu, Hanyin Cheng +5Time Series Forecasting with Covariates Time Series Forecasting
East China Normal University · Aalborg University
$t_0$ : A Time-Series Foundation Model for Forecasting with Context
Sep 21, 2026Lucas Meyer, Claudio Sole, Huikan Xiang +6Time Series Forecasting Zero-Shot Time Series Forecasting
The Forecasting Company · Macrocosm
Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting
Jul 24, 2026Wan Zhang, Qinjie Lin, Chan Lee +3Multivariate Time Series Forecasting Time Series Forecasting
The AMSS Center of Forecasting Science, Chinese Academy of Sciences, Beijing, China · Department of Computer Science, Northwestern University, Evanston, IL · Department of Statistics and Data Science, Northwestern University, Evanston, IL +1
Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates
Jul 5, 2026Hao Hu, Xue-shan AiNeural Network Robustness Time Series Forecasting
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China · Hubei Key Laboratory of Water System Science for Sponge City Construction (Wuhan University), Wuhan 430072, China
TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
Jul 1, 2026Patrick Podest, Marco Pichler, Elias Bürger +7Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
ELLIS Unit Linz, LIT AI Lab & Institute for Machine Learning, JKU Linz, Austria · NXAI Lab, Linz, Austria · NXAI GmbH, Linz, Austria +1
CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting
Jun 9, 2026Yosuke Yamaguchi, Issei Suemitsu, Yuki Kajihara +1Synthetic Data Pretraining Zero-Shot Time Series Forecasting
Research & Development Group, Hitachi Ltd., Tokyo, Japan
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
May 12, 2026Gaspard Berthelier, Mariia Baranova, Andrei-Tiberiu Pantea +4Multivariate Time Series Forecasting Time Series Forecasting
EDF R&D, Palaiseau · Inria Sophia-Antipolis, Université Côte d’Azur · Université Paris Cit'e

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EnergyBench: a live, gate-aligned benchmark of day-ahead forecasts for European power markets

Evgenii Genov, Ander Martinez Alonso, Christoph Bergmeir

EVERGi Research Group, Flanders Alliance for Climate Technology (FACT), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium · DTU Wind and Energy Systems, Technical University of Denmark · Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain+1

Forecasting Benchmarks Electricity Price Forecasting Time-Series Foundation Models Time Series Forecasting with Covariates Zero-Shot Time Series Forecasting

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Energy forecasting models are usually compared on backtests and fixed public datasets. Backtests can use data that did not yet exist when the forecast would have been made, and a fixed test set, reused across studies and possibly contained in the pretraining data of foundation models, invites overfitting to the benchmark. EnergyBench evaluates every entry live instead. Each day at 11:30 CET/CEST, thirty minutes before the day-ahead (SDAC) gate closes, every model forecasts the next day's price, wind and solar generation and load on the 15-minute grid for six bidding zones (BE, NL, FR, DE-LU, DK1, DK2). Unlike existing live benchmarks, EnergyBench runs the models itself, on one information set that it freezes at the cut-off and publishes, including three numerical weather prediction models, so that foundation models can be compared with and without the same weather covariates under the actual market deadline. Errors are pooled per series against a seasonal-naive baseline, and models are ranked by a Bradley-Terry rating on the Elo scale with a day-bootstrap interval. The field spans baselines, linear and gradient-boosted models on the frozen weather, eighteen time-series foundation models from eleven families, ensembles, and the grid operators' own forecasts. Over the first thirty-two live days, the covariates reduced the error of each of the ten foundation models run with and without them by 37-51% on matched races (64-73% on wind, 48-61% on solar). Used zero-shot, these models match or outperform a LightGBM model trained on the same covariates. Model size, from 3M to 331M parameters, has a much smaller effect, and median ensembles of the covariate models rank highest. The grid operators' pre-gate forecasts remain ahead on wind and solar, where the best single foundation models have 16-22% more error; on load the gap is 4-6%, within the intervals.