Corpus ID: 232380379

Domain Specific Concept Drift Detectors for Predicting Financial Time Series

@article{Neri2021DomainSC,
  title={Domain Specific Concept Drift Detectors for Predicting Financial Time Series},
  author={Filippo Neri},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.14079}
}
  • F. Neri
  • Published 2021
  • Computer Science, Economics
  • ArXiv
Concept drift detectors allow learning systems to maintain good accuracy on non-stationary data streams. Financial time series are an instance of non-stationary data streams whose concept drifts (market phases) are so important to affect investment decisions worldwide. This paper studies how concept drift detectors behave when applied to financial time series. General results are: a) concept drift detectors usually improve runtime over continuous learning, b) their computational cost is usually… Expand
2 Citations

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