Online Time Series Forecasting

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8 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting
Sep 25, 2026Takumi Fujimoto, Hiroaki NishiOnline Time Series Forecasting Multivariate Time Series Forecasting
School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan · Department of System Design, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Sep 21, 2026Yifan Hu, Xilin Dai, Zhiyuan Qu +4Online Time Series Forecasting Time Series Forecasting
Ant International · Tsinghua University · The Chinese University of Hong Kong
LoRA-RC: Reservoir Computing with Low-Rank Adaptation
Sep 11, 2026Wenbin WanOnline Time Series Forecasting Dynamical Systems
Department of Mechanical Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Compositional Spectral Prompts for LLM-based Online Time Series Forecasting
Sep 2, 2026Seungyoon Choi, Hyunchul Kim, Jae-Gil Lee +1Online Time Series Forecasting Time Series Forecasting
KAIST Daejeon, Republic of Korea
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Jun 3, 2026Yiming Lu, Sihang Zeng, Zhengxu Tang +3Online Time Series Forecasting Epidemic Forecasting
Emory University · University of Washington
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
May 27, 2026Haonan Wen, Hanyang Chen, Songhe FengIrregular Time-Series Modeling Online Time Series Forecasting
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · Tangshan Research Institute, Beijing Jiaotong University Tangshan, China
Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
May 11, 2026Btissame El Mahtout, Florian ZielOnline Time Series Forecasting Time Series Forecasting
Chair of Data Science in Energy and Environment, University of Duisburg-Essen, Germany · Chair of Econometrics and Statistics, Technical University of Dortmund, Germany
Dynamic Multi-period Experts for Online Time Series Forecasting
Mar 10, 2026Seungha Hong, Sukang Chae, Suyeon Kim +2Online Time Series Forecasting Time Series Forecasting
Pohang University of Science and Technology, Pohang, Republic of Korea · Kyungpook National University, Daegu, Republic of Korea

Sep 25, 2026J/K move · Enter open · S save

EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting

Takumi Fujimoto, Hiroaki Nishi

School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan · Department of System Design, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan

Online Time Series Forecasting Multivariate Time Series Forecasting Residual Learning Time Series Forecasting

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Completed forecasts provide residual feedback, but retaining full residual blocks increases auxiliary state. We propose Endpoint-Preserving Online Correction (EPOC) with a compressed residual state. It stores low-order discrete cosine transform (DCT) coefficients and the final value of the preceding residual block. Each channel shares the endpoint across component-wise online ridge regressions using current-forecast coefficients. We evaluate eight multivariate series, DLinear and PatchTST, three seeds, and two training variants: 96 matched fixed-base conditions at a 24-step horizon. EPOC achieves mean condition-wise reductions in mean squared error (MSE) and mean absolute error (MAE) of 15.40% and 9.35% from the uncorrected base, respectively, with a median of 6,352 B in retained auxiliary arrays. EPOC also outperforms the

δ

-Adapter, COSA, FAC, and OMPB in paired MSE on most conditions while retaining less state. Full ELF achieves the largest mean MSE reduction, 19.29%, but its median retained state is 474,048 B (

\times

75 relative to EPOC). Equal-size summary controls favor the endpoint by 1.65--2.20% in paired MSE; a coefficient-reconstructed endpoint yields similar accuracy to the observed endpoint, highlighting its role as a shared input. Increasing the retained DCT component count from 4 to 8 adds 1.00 percentage point of MSE reduction for 5,728 B. On jointly trained bases, EPOC lowers MSE by 16.69--20.15% relative to globally blended TEFL-style adapters applied to the same base. Code and numerical records are available at https://github.com/keiotakmin/endpoint-preserving-residual-correction.