Application of Adaptive Machine Learning in Non-Stationary Environments

Authors

  • Yuanfang Yang Southern Methodist University, Lyle School of Engineering, USA Author
  • Haopeng Zhao New York University, USA Author
  • Jiaye Wan Independent Researcher, China Author
  • Mingxia Fu Amazon com Inc, USA Author
  • Zehan Wang University of Maryland, College Park, USA Author
  • Bingying Liu Capital One, USA Author
  • Ge Shi Independent Researcher, USA Author

DOI:

https://doi.org/10.60087/jklst.vol3.n4.p68

Keywords:

Adaptive machine learning; Non-stationary environments; Online learning; Incremental learning; Financial market forecasting; Industrial equipment monitoring; Robustness; Anomaly detection; Multi-source data fusion

Abstract

In the current data-driven era, the application of adaptive machine learning in non-stationary environments has become particularly important. This paper explores the basic concepts of adaptive machine learning and its applications in financial market forecasting and industrial equipment monitoring, demonstrating its superior performance in high-noise, dynamic environments. The research results indicate that adaptive machine learning models significantly improve prediction accuracy, response speed, and robustness through strategies such as online learning and incremental learning. In financial market forecasting, the mean squared error (MSE) was reduced to 0.015, and the fault detection accuracy in industrial equipment monitoring increased to 95%. The paper also proposes future research directions, including multi-source data fusion, anomaly detection, computational efficiency enhancement, and model interpretability. Adaptive machine learning technology not only enriches the theoretical framework of machine learning but also provides new solutions for practical applications in various fields, paving the way for further development of intelligent systems.

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Published

02-09-2024

How to Cite

Yang , Y. ., Zhao , H. ., Wan, J., Mingxia , F., Wang, Z. ., Liu , B. ., & Shi, G. . (2024). Application of Adaptive Machine Learning in Non-Stationary Environments. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4), 68-77. https://doi.org/10.60087/jklst.vol3.n4.p68

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