The paper investigates when online adaptation benefits edge time‑series forecasting under distribution drift, using a leakage‑free streaming protocol on six public multivariate datasets. It shows that the warmup budget for static baselines and the choice of learning rate can bias perceived adaptation gains, and that a validation‑only procedure selecting warmup and optimizer rates yields Adam outperforming SGD with momentum in most settings. The study also examines accuracy versus adaptation‑state memory and per‑update latency for different adaptation strategies, highlighting parameter‑efficient variants that are nondominated on the memory axis.
By Takumi Fujimoto, Hiroaki Nishi
CoMemNet is a Continual Memory Network designed for traffic prediction on evolving sensor networks. It combines an online branch that adapts to current data with an exponential‑moving‑average target branch for stable reference, and uses a Wasserstein‑based drift sampler to selectively update drift‑sensitive nodes. A lightweight temporal memory replay buffer stores compact states, allowing efficient adaptation without traversing all historical data. Experiments on PeMS datasets show that CoMemNet maintains stable accuracy and outperforms retraining baselines in both prediction performance and training time.
By Mei Wu, Wenchao Weng, Wenxin Su, Wenjie Tang, Wei Zhou
The paper introduces the Stream Cruise Control Method (SCCM), a framework for detecting and adapting to concept drift in online regression. SCCM performs early-response drift detection, quantifies drift magnitude, applies KPI-window-based thresholding to reduce false alarms, dynamically tunes hyperparameters, and recalibrates models, all within an in-memory design for real-time operation. Evaluations on synthetic and real-world datasets demonstrate that SCCM improves predictive performance compared to eight baseline detector–adaptation methods.
By Mohammad Abu-Shaira, Weishi Shi
arXiv:2608.30923v1 Announce Type: cross
Abstract: Stream learning is commonly evaluated through predictive performance and adaptation to concept drift. However, sustained operation of a stream learne...
By Sebastian Buschj\"ager, Nuwan Gunasekara, Heitor Murilo Gomes
arXiv:2606. 09430v1 Announce Type: cross Abstract: Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass constraints and without any explicit task identifiers.
By Mingqi Yuan, Xiaoquan Sun, Shihao Luo, Jiayu Chen
arXiv:2510. 16898v2 Announce Type: replace-cross Abstract: Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers.
By Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi
arXiv:2511. 05420v2 Announce Type: replace-cross Abstract: As smart grids evolve to meet growing energy demands and modern operational challenges, the ability to accurately predict faults becomes increasingly critical.
By Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze, Gian Antonio Susto, Chuadhry Mujeeb Ahmed, Mirco Rampazzo
arXiv:2608. 14698v1 Announce Type: cross Abstract: Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding 1000~USD per node, making distributed deployments economically inaccessible.
By Erick Michel Lara Pinal, Abhinav Das, Stephan Schl\"uter
AsyncCouple-Flow introduces a new framework for multi‑modal spatio‑temporal forecasting that tackles three key challenges: differing sampling rates, missing modalities, and autoregressive error accumulation. It employs a Modality‑Aware Token Sparsification module to produce equal‑length sequences, an Asynchronous Cross‑Modal Coupling Graph to fuse data under arbitrary asynchrony and missingness, and a Flow‑Matching Forecasting Head that models multi‑step prediction as a conditional ODE. Experiments on weather and traffic datasets demonstrate that the method outperforms state‑of‑the‑art baselines and remains robust even when up to two modalities are missing.
By Zhixiang Wu, Yining Liu, Bo Zhao, Szu-Yu Chen, Huiran Duan, Chu Lin, Chuanguang Yang
arXiv:2608. 19488v1 Announce Type: new Abstract: Production machine learning systems degrade under concept drift, yet practitioners have little principled guidance on when to retrain.
By Sawan Dasari
arXiv:2608. 02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over time, known as data drift and concept drift, respectively.
By Swapn Shah, Keith Burghardt
arXiv:2609.15763v1 Announce Type: cross
Abstract: Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it impor...
By Yuxuan Sun, Yuxuan Bai, Tan Chen, Sheng Zhou, Zhisheng Niu