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Title: FEDD: Feature Extraction for Explicit Concept Drift Detection in Time Series
Authors: Cavalcante, R.
Minku, Leandro Lei
Oliveira, A.
First Published: 2016
Presented at: 2016 International Joint Conference on Neural Networks (IJCNN 2016)
Publisher: Institute of Electrical and Electronics Engineers (IEEE), United States
Citation: Proceedings, 2016 International Joint Conference on Neural Networks (IJCNN 2016) (Accepted, In Press)
Abstract: A time series is a sequence of observations col- lected over fixed sampling intervals. Several real-world dynamic processes can be modeled as a time series, such as stock price movements, exchange rates, temperatures, among others. As a special kind of data stream, a time series may present concept drift, which affects negatively time series analysis and forecasting. Explicit drift detection methods based on monitoring the time series features may provide a better understanding of how concepts evolve over time than methods based on monitoring the forecasting error of a base predictor. In this paper, we propose an online explicit drift detection method that identifies concept drifts in time series by monitoring time series features, called Feature Extraction for Explicit Concept Drift Detection (FEDD). Computational experiments showed that FEDD performed better than error-based approaches in several linear and nonlinear artificial time series with abrupt and gradual concept drifts.
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © 2016, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The file attached to this record is distributed under the Creative Commons “Attribution Non-Commercial No Derivatives” licence, further details of which can be found via the following link:
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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