Big Data Time Series Stream Data Segmentation Methods
Dima Alberg · Advances in computer and electrical engineering book series · 2018
In this chapter, the authors introduce the interval sliding window (ISW) and interval sliding window and bottom up (ISWAB) algorithms, which are applicable to big data numerical time series data streams and use as input the confidence level parameter rather than the maximum error threshold. The proposed algorithms have two advantages: first, they allow performance comparisons across different time series data streams without changing the algorithm settings, and second, they do not require preprocessing the original time series data stream in order to determine heuristically the reasonable error value. These improvements are very efficient and important in context of big data tine series data streams processing. Finally, an empirical evaluation was performed on two types of time series data.