Improvement algorithms of perceptually important point identification for time series data mining
Tak-chung Fu, Ying Kit Hung, Fu-Lai Chung · 2017
In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is proposed for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful applications in the past years, it is worth to further explore the opportunity to apply PIP in time series “Big Data”. However, the performance of PIP identification is always considered as the limitation when dealing with “Big” time series data. In this paper, two improvement algorithms namely Caching and Splitting algorithms are proposed. Significant improvement in term of speed is obtained by these improvement algorithms.