# Domain Specific Concept Drift Detectors for Predicting Financial Time Series

arXiv: https://arxiv.org/abs/2103.14079 · HTML: https://mlsanity.com/paper/2103.14079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Reactive Soft Prototype Computing for Concept Drift Streams](https://mlsanity.com/paper/2007.05432/) (2020)
- [How to Identify Investor's types in real financial markets by means of agent based simulation](https://mlsanity.com/paper/2101.03127/) (2020)

## Built on by (most cited first, 1 of 1)

- [Mapping Learning Algorithms on Data, a useful step for optimizing performances and their comparison](https://mlsanity.com/paper/2107.06981/) (2021)

## Abstract (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 the runtime over continuous learning, b) their computational cost is usually a fraction of the learning and prediction steps of even basic learners, c) it is important to study concept drift detectors in combination with the learning systems they will operate with, and d) concept drift detectors can be directly applied to the time series of raw financial data and not only to the model's accuracy one. Moreover, the study introduces three simple concept drift detectors, tailored to financial time series, and shows that two of them can be at least as effective as the most sophisticated ones from the state of the art when applied to financial time series. Currently submitted to Pattern Recognition