arXiv Machine Learning August 19, 2026 By Tianxiang Zhan, Ming Jin, Yuanpeng He, Yuxuan Liang, Shirui Pan

Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2506. 14790v3 Announce Type: replace Abstract: Recurring concept drift is pervasive in real-world online time series, where the underlying data-generating process repeatedly alternates between a small set of regimes, most notably daily or seasonal cycles that dominate energy, traffic, and weather patterns, and is therefore a central obstacle to reliable long-horizon forecasting.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jun 4

Stationarity-Aware Retrieval-Augmented Time Series Forecasting

arXiv:2606. 04135v1 Announce Type: new Abstract: Time series forecasting relies on historical patterns, but real-world series often exhibit non-stationarity and regime shifts that challenge fully parametric forecasters.

By Shiqiao Zhou, Holger Sch\"oner, Zipeng Wu, Edouard Fouch\'e, IAG Wilson, Shuo Wang

rag

arXiv AI

Jul 22

Black-Mamba: Biologically-Inspired Leaky Accumulation for Conceptual Knowledge under Distribution Drift

arXiv:2607. 18899v1 Announce Type: new Abstract: Forecasting under real-world conditions is inherently non-stationary, as the conditional distribution of future observations evolves over time.

By Giuseppe Soriano, Nicola Tonellotto, Alberto Gotta

benchmarks

arXiv Machine Learning

Jun 25

Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series

arXiv:2606. 24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors.

By Yujiang He, Frederic Uhrweiller, Bernhard Sick

arXiv AI

Jun 6

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

arXiv:2606. 05513v1 Announce Type: new Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time.

By Yiming Lu, Sihang Zeng, Zhengxu Tang, Max Lau, Fei Liu, Wei Jin

llms agents

arXiv AI

Jun 16

TS-Memory: Plug-and-Play Memory for Time Series Foundation Models

arXiv:2602. 11550v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) achieve strong zero-shot forecasting through large-scale pre-training, but adapting them to downstream domains under distribution shift remains challenging.

By Sisuo Lyu, Siru Zhong, Tiegang Chen, Weilin Ruan, Qingxiang Liu, Taiqiang Lv, Qingsong Wen, Raymond Chi-Wing Wong, Yuxuan Liang

efficiency benchmarks

arXiv AI

Jun 10

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning

arXiv:2602. 03164v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications.

By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Shuo Yu, Yaguo Liu, Qi Liu, Shijin Wang

llms

Related stories

Stationarity-Aware Retrieval-Augmented Time Series Forecasting

Black-Mamba: Biologically-Inspired Leaky Accumulation for Conceptual Knowledge under Distribution Drift

Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

TS-Memory: Plug-and-Play Memory for Time Series Foundation Models

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning