Explainable Time Series Forecasting

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Overview

Explainable time series forecasting studies how to show which past observations, variables, contexts, or learned dynamics shape predictions across future horizons. Approaches include post-hoc methods such as SHAP and counterfactuals, interpretable decompositions and symbolic equations, causal or concept-based models, and language-model reasoning. Methods are tested on settings such as energy demand, health, traffic, and industrial forecasting, where temporal dependence, multiple variables, uncertainty, and changing regimes matter. A central challenge is ensuring explanations are faithful and stable—not merely plausible—while preserving forecast accuracy, especially when different horizons rely on different evidence.

Latest papers

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61 papers12 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

  1. TempShap: When and How Much a Feature Matters in Time-Series Forecasting

    Oct 23, 2026Chi Zhao, Anastasiia Chelnokova, Jing Liu +1Explainable Time Series ForecastingFeature Attribution

    Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg 199034, Russia

  2. A Model-Agnostic Correction Framework for Short-Term Building Load Forecasting

    Oct 22, 2026Shiqiao Zhou, Huanbo Lyu, Holger Schöner +5Electricity Load ForecastingTime Series Forecasting

    School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, West Midlands, United Kingdom · Siemens AG, Munich, 80333, Bavaria, Germany · School of Mathematics, University of Birmingham, Edgbaston, Birmingham, B15 2TT, West Midlands, United Kingdom +1

  3. SSMCGM-Stream: A Streaming State-Space Model for Personalized Diabetes Management

    Oct 20, 2026Shakson Isaac, Chirag PatelBlood Glucose ForecastingExplainable Time Series Forecasting

    Department of Biomedical Informatics, Harvard Medical School

  4. A Hybrid TFT-NBEATS Framework for Robust Multi-Horizon Photovoltaic Power Forecasting

    Oct 19, 2026Shafi Bin Sultan, Sabik Bin Sultan, Mohammad Tajrian Arafat Alvi +2Solar Power ForecastingTime Series Forecasting

    St. Joseph Higher Secondary School, Dhaka, Bangladesh · Bangladesh Air Force Shaheen College, Kurmitolla, Dhaka, Bangladesh · Computer Science & Engineering, Jahangirnagar University, Savar, Bangladesh +2

  5. CVTA: Cross-Variable Temporal Attention for Multivariate Irregular Time Series Prediction

    Oct 18, 2026Ankitkumar Joshi, Milos HauskrechtMultivariate Time Series ForecastingTime Series Forecasting

    Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260

  6. Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs

    Oct 15, 2026Jinyi Duan, Qian Li, Biao TangEpidemic ForecastingSpatial Epidemic Modeling

    School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China . · School of Mathematics and Statistics, Xi’an University of Technology, Xi’an, 710054, People’s Republic of China . · Center for Intersection of Mathematics and Life Sciences, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China .

  7. Sera: Semantic Representation Aggregation with Knowledge and LLM for Reliable and Interpretable Battery Health Forecasting

    Oct 8, 2026Jiawei Li, Fang Liu, Wei Zhang +3Semantic Representation LearningTime Series Forecasting

    Infocomm Technology Cluster, Singapore Institute of Technology, Singapore · School of Science and Technology, Singapore University of Social Sciences, Singapore · College of Smart Energy, Shanghai Jiao Tong University, Shanghai, China +2

  8. Textual Environmental Context and Spatial Graphs for LLM-Based Regional SST Forecasting

    Oct 6, 2026Xiong Li, Xiaowei Zhou, Yanwei Yu +2Spatiotemporal ForecastingLLM-Based Time Series Forecasting

    Ocean University of China, Qingdao, China

  9. When Attention Does Not Explain the Peak: Temporal Reference vs. Forecast Output in Attention-Based Time-Series Forecasting

    Oct 5, 2026Yuji Akamatsu, Takao YamanakaExplainable Time Series ForecastingCross-Attention

    Sophia University, Tokyo, Japan

  10. TSAE: Structured Sparse Autoencoders for Interpreting Time-Series Forecasting Models

    Oct 4, 2026Baoxi Liu, Yi XieExplainable Time Series ForecastingSparse Autoencoders

  11. Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods

    Sep 28, 2026Jia Long Bao, Ali Mohammed Mansoor Alsahag, Seyed Sahand Mohammadi ZiabariExplainable Time Series ForecastingTime Series Forecasting

  12. Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models

    Sep 28, 2026Kang Yang, Gaofeng Dong, Liying Han +1Explainable Time Series ForecastingRepresentation Probing

    Department of Electrical and Computer Engineering University of California, Los Angeles

  13. T-MoXAI: A Hierarchical Explainability Framework for Temporal Multimodal Data

    Sep 27, 2026Ali Inha, Mo Vali, Saaliha Vali +2Explainable Time Series ForecastingMultimodal Model Interpretability

    Department of Computer Science, University of Cambridge, Cambridge, UK · Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK · Imperial College Healthcare NHS Trust, London, UK +1

  14. CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting

    Sep 27, 2026Ya-Wen Wu, Meng-Fen Chiang, Kuang-Da Wang +1Financial ForecastingTime Series Forecasting

    National Yang Ming Chiao Tung University, Hsinchu, Taiwan

  15. Rethinking Cross-Channel Importance in Time-Series Forecasting

    Sep 26, 2026Yong-Hoon Choi, Kwang-Hyun Park, Youngjin ChoMultivariate Time Series ForecastingExplainable Time Series Forecasting

    Division of Robotics, Kwangwoon University Seoul 01897, Republic of Korea

  16. TAC-Time: Texts as Channels For Multimodal Time Series Forecasting

    Sep 21, 2026Jiayi Liang, Xiaotian Gu, Xinyu Xie +2Multivariate Time Series ForecastingExplainable Time Series Forecasting

    East China Normal University Shanghai, China

  17. SETTer: Sparse-Encoder Transformer for Long-term Multivariate Time Series Forecasting

    Sep 17, 2026Abraham Ezema, Chijioke Eze, Ferdinanda Ponci +1Multivariate Time Series ForecastingLong-Term Time Series Forecasting

    Institute for Automation of Complex Power Systems, RWTH Aachen University, 52064 Aachen, Germany · RWTH Aachen University, 52064 Aachen, Germany · institute for Monitoring and Distributed Control for Power Systems Teaching and Research Area, RWTH Aachen University, 52064 Aachen, Germany +1

  18. Explaining Time Series Forecasting with Horizon-Resolved Attribution

    Sep 11, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Explainable Time Series ForecastingFeature Attribution

    LG AI Research

  19. Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models

    Sep 5, 2026Manuel Naviglio, Fabrizio LilloQuantitative FinanceExplainable Time Series Forecasting

    Scuola Normale Superiore, Pisa, Italy

  20. How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study

    Sep 4, 2026Glib KechynExplainable Time Series ForecastingFeature Attribution

  21. A causal graph-informed temporal convolution architecture for interpretable retail electricity price forecasting

    Aug 26, 2026Yufan Ji, Abdollah Shafieezadeh, Noah DormadyElectricity Price ForecastingExplainable Time Series Forecasting

    Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA · John Glenn College of Public Affairs, The Ohio State University, Columbus, 43210, OH, USA

  22. Towards A Unified Information Bottleneck Framework for Time Series Explanations

    Aug 26, 2026Xu Zheng, Zichuan Liu, Zhuomin Chen +7Explainable Time Series ForecastingExplainable Artificial Intelligence

    Florida International University · Carnegie Mellon University · University of Houston +2

  23. NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates

    Aug 25, 2026Muyan Anna Li, Manikandan Ravikiran, Aditi GautamExplainable Time Series ForecastingInterpretable ML

    NVIDIA, USA

  24. ConceptTS: LLM-Guided Concept Bottlenecks for Interpretable Multivariate Time-Series Forecasting

    Aug 21, 2026Yichen Jiang, Yueqiao Chen, Dongyu LiuMultivariate Time Series ForecastingExplainable Time Series Forecasting

    Department of Electrical Engineering Stanford University Stanford, CA, USA · Department of Computer Science University of California, Davis Davis, CA, USA

  25. Fuzzy-MoE: Interpretable Regime-Conditioned Expert Routing for Non-Stationary Multivariate Time Series Forecasting

    Aug 21, 2026Lan Guo, Jie Xiao, Zhao Su +5Multivariate Time Series ForecastingNon-Stationary Time Series Forecasting

    School of Information Science and Engineering, Lanzhou University · School of Computing and Information Technology, University of Wollongong · Department of Data Science and AI, Monash University

  26. Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems

    Aug 18, 2026Jonas Braun, Fabian Fischbach, Daniel Köglmayr +2Time-Series Causal DiscoveryExplainable Time Series Forecasting

    Ludwig-Maximilians-Universität München, Faculty of Physics, Geschwister-Scholl-Platz 1, 80539 Munich, Germany · Institut für Theoretische Physik, Universität zu Köln, Zülpicher Strasse 77, 50937 Cologne, Germany · Institut für KI-Sicherheit, Deutsches Zentrum für Luft- und Raumfahrt (DLR), St. Augustin & Ulm, Germany +2

  27. REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

    Aug 10, 2026Xu Zhang, Chang Xu, Hui Sun +5RL for Language ModelsExplainable Time Series Forecasting

    Fudan University, Shanghai, China · Microsoft Research, Beijing, China · Nankai University, Tianjin, China +2

  28. Traceable Multi-Agent System for Knowledge-Based Forecasting

    Aug 4, 2026Junhyeok Kang, Sangjun Han, Hyeokjun Choe +1Explainable Time Series ForecastingMulti-Agent Orchestration

    LG AI Research · Seoul, Republic of Korea

  29. ReasonCast: Towards Explainable Time Series Forecasting with Reasoning

    Aug 3, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Explainable Time Series ForecastingTime Series Reasoning

    LG AI Research

  30. Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

    Jul 30, 2026Xu Zheng, Wei Cheng, Zhuomin Chen +3Multivariate Time Series ForecastingExplainable Time Series Forecasting

    Florida International University, Miami, FL, USA · NEC Laboratories America, Princeton, NJ, USA · University of Houston, Houston, TX, USA +1