mlsanityData to 7 Oct 2026

Online Learning for Time Series Prediction

arXiv 1302.6927 ↗First posted 27 Feb 2013cs.LGcited by 33

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Abstract (arXiv)

In this paper we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show that our algorithm's performances asymptotically approaches the performance of the best ARMA model in hindsight.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2013.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 6; 2024: 7; 2025: 6; 2026: 2.

33 papers.

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  1. 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensemblingcited by 71
  2. 2022Learning Fast and Slow for Online Time Series Forecastingcited by 43
  3. 2021Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Gamescited by 18
  4. 2022Online Dynamics Learning for Predictive Control with an Application to Aerial Robotscited by 17
  5. 2023Can Transformers Learn Optimal Filtering for Unknown Systems?cited by 15
  6. 2023Unconstrained Dynamic Regret via Sparse Codingcited by 11
  7. 2024Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adaptcited by 11
  8. 2021Stochastic Online Convex Optimization. Application to probabilistic time series forecastingcited by 8
  9. 2022Adversarially Robust PAC Learnability of Real-Valued Functionscited by 8
  10. 2024Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Datacited by 6
  11. 2025Universal Sequence Preconditioningcited by 6
  12. 2023Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Predictioncited by 5
21 more
  1. 2024Predictive Linear Online Tracking for Unknown Targetscited by 5
  2. 2025Universal Learning of Nonlinear Dynamicscited by 5
  3. 2022Fairness in Forecasting of Observations of Linear Dynamical Systemscited by 4
  4. 2025Research Program: Theory of Learning in Dynamical Systemscited by 4
  5. 2022ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecastingcited by 3
  6. 2022Optimistic Optimisation of Composite Objective with Exponentiated Updatecited by 3
  7. 2022Learning from time-dependent streaming data with online stochastic algorithmscited by 2
  8. 2023Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecastingcited by 2
  9. 2024Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shiftscited by 2
  10. 2025Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Cachingcited by 2
  11. 2025Model-free Online Learning for the Kalman Filter: Forgetting Factor and Logarithmic Regretcited by 2
  12. 2025Online Time Series Forecasting with Theoretical Guaranteescited by 2
  13. 2021POLA: Online Time Series Prediction by Adaptive Learning Ratescited by 1
  14. 2024The Complexity of Sequential Prediction in Dynamical Systemscited by 1
  15. 2024OML-AD: Online Machine Learning for Anomaly Detection in Time Series Datacited by 1
  16. 2021WAX-ML: A Python library for machine learning and feedback loops on streaming datacited by 0
  17. 2021Personalized Online Machine Learningcited by 0
  18. 2023Online estimation methods for irregular autoregressive modelscited by 0
  19. 2024Quantum Algorithm for Sparse Online Learning with Truncated Gradient Descentcited by 0
  20. 2026Online Continual Learning for Time Series: a Natural Score-driven Approachcited by 0
  21. 2026Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibrationcited by 0