Detecting Volatility Shift in Data Streams
- 1 December 2014
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 863-868
- https://doi.org/10.1109/icdm.2014.50
Abstract
Current drift detection techniques detect a change in distribution within a stream. However, there are no current techniques that analyze the change in the rate of these detected changes. We coin the term stream volatility, to describe the rate of changes in a stream. A stream has a high volatility if changes are detected frequently and has a low volatility if changes are detected infrequently. We are particularly interested in a volatility shift which is a change in the rate of change (e.g. From high volatility to low volatility). We introduce and define the concept of stream volatility, and propose a novel technique to detect volatility on data streams in the presence of concept drifts. In the experiments we show our algorithm to be both fast and efficient. We also propose a new algorithm for drift detection called SEED that is faster and more memory efficient than the existing state-of-the-art drift detection approach. A faster drift detection algorithm has a flow-on benefit to the subsequent volatility detection stage because both algorithms run concurrently on the data stream.Keywords
This publication has 7 references indexed in Scilit:
- Stream Classification with Recurring and Novel Class Detection Using Class-Based EnsemblePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Mining Recurring Concept Drifts with Limited Labeled Streaming DataACM Transactions on Intelligent Systems and Technology, 2012
- Detecting Recurring and Novel Classes in Concept-Drifting Data StreamsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Tracking recurring contexts using ensemble classifiers: an application to email filteringKnowledge and Information Systems, 2009
- Learning from Time-Changing Data with Adaptive WindowingPublished by Society for Industrial & Applied Mathematics (SIAM) ,2007
- Detecting Change in Data StreamsPublished by Elsevier BV ,2004
- Continuous Inspection SchemesBiometrika, 1954