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Towards Burst Detection for Non-Stationary Stream Data [work in progress]
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@MISC{Klan_towardsburst,
author = {Daniel Klan and Marcel Karnstedt and Christian Pölitz and Kai-uwe Sattler},
title = {Towards Burst Detection for Non-Stationary Stream Data [work in progress]},
year = {}
}
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Abstract
Detecting bursts in data streams is an important and challenging task, especially in stock market, traffic control or sensor network streams. Burst detection means the identification of non regular behavior within data streams. A specifically crucial challenge on burst detection is to identify bursts in the case of non-stationary data. One approach is to apply thresholds to discover such bursts. In this paper, we propose a new approach to dynamically identify suitable thresholds using techniques known from time series forecasting. We present fundamentals and discuss requirements for threshold-based burst detection on stream data containing arbitrary trends and periods. 1
Keyphrases
towards burst detection non-stationary stream data work burst detection data stream suitable threshold discus requirement non-stationary data stock market stream data time series forecasting sensor network stream challenging task new approach crucial challenge present fundamental threshold-based burst detection traffic control arbitrary trend non regular behavior
