mlsanityData to 14 Oct 2026

River: machine learning for streaming data in Python

arXiv 2012.04740 ↗First posted 8 Dec 2020cs.LG cs.AI cs.MScited by 98

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Abstract (arXiv)

River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.

Key facts. Cited by 98 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 13; 2023: 15; 2024: 22; 2025: 19; 2026: 20.

98 papers.

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  1. 2021TinyOL: TinyML with Online-Learning on Microcontrollerscited by 65
  2. 2022A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental frameworkcited by 39
  3. 2025Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learningcited by 14
  4. 2021Adversarial Robustness of Streaming Algorithms through Importance Samplingcited by 13
  5. 2024German Text Embedding Clustering Benchmarkcited by 13
  6. 2022Online AutoML: An adaptive AutoML framework for online learningcited by 11
  7. 2023A comprehensive analysis of concept drift locality in data streamscited by 10
  8. 2021A Survey on Spatio-temporal Data Analytics Systemscited by 9
  9. 2021ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systemscited by 9
  10. 2021pyWATTS: Python Workflow Automation Tool for Time Seriescited by 9
  11. 2022Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streamscited by 9
  12. 2022A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systemscited by 8
38 more
  1. 2024Early Concept Drift Detection via Prediction Uncertaintycited by 8
  2. 2023One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environmentscited by 7
  3. 2026AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selectioncited by 6
  4. 2021MRCpy: A Library for Minimax Risk Classifierscited by 5
  5. 2023RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventionscited by 5
  6. 2025Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysiscited by 5
  7. 2025Online hierarchical partitioning of the output space in extreme multi-label data streamcited by 5
  8. 2022Standardized Evaluation of Machine Learning Methods for Evolving Data Streamscited by 4
  9. 2022A Survey on Uncertainty Toolkits for Deep Learningcited by 4
  10. 2023TinyReptile: TinyML with Federated Meta-Learningcited by 4
  11. 2024Calibrated Probabilistic Forecasts for Arbitrary Sequencescited by 4
  12. 2022Online Agnostic Multiclass Boostingcited by 3
  13. 2023iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streamscited by 3
  14. 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streamscited by 3
  15. 2024Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learningcited by 3
  16. 2024Hoeffding adaptive trees for multi-label classification on data streamscited by 3
  17. 2024Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streamscited by 3
  18. 2023MLHOps: Machine Learning for Healthcare Operationscited by 2
  19. 2023TinyMetaFed: Efficient Federated Meta-Learning for TinyMLcited by 2
  20. 2024ActDroid: An active learning framework for Android malware detectioncited by 2
  21. 2025CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Pythoncited by 2
  22. 2025Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networkscited by 2
  23. 2026A Practical Guide to Streaming Continual Learningcited by 2
  24. 2022BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractorcited by 1
  25. 2022Frouros: A Python library for drift detection in machine learning systemscited by 1
  26. 2022Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Frameworkcited by 1
  27. 2022LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devicescited by 1
  28. 2023PyTorch Hyperparameter Tuning - A Tutorial for spotPythoncited by 1
  29. 2023OPTWIN: Drift identification with optimal sub-windowscited by 1
  30. 2024A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Datacited by 1
  31. 2024Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysiscited by 1
  32. 2024OML-AD: Online Machine Learning for Anomaly Detection in Time Series Datacited by 1
  33. 2024Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streamscited by 1
  34. 2024A Framework for Streaming Event-Log Prediction in Business Processescited by 1
  35. 2025Adaptive Detection of Software Aging under Workload Shiftcited by 1
  36. 2025Online conformal inference with retrospective adjustment for faster adaptation to distribution shiftcited by 1
  37. 2025ARES: Anomaly Recognition Model For Edge Streamscited by 1
  38. 2026Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streamscited by 1

50 most cited of 98 All 98 (JSON)