TY - GEN
T1 - Improvement algorithms of perceptually important point identification for time series data mining
AU - Fu, Tak Chung
AU - Hung, Ying Kit
AU - Chung, Fu Lai Korris
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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.
AB - 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.
KW - Perceptually Important Point identification
KW - performance analysis
KW - PIP
KW - time series data mining
UR - http://www.scopus.com/inward/record.url?scp=85050869750&partnerID=8YFLogxK
U2 - 10.1109/ISCMI.2017.8279589
DO - 10.1109/ISCMI.2017.8279589
M3 - Conference article published in proceeding or book
AN - SCOPUS:85050869750
T3 - IEEE 4th International Conference on Soft Computing and Machine Intelligence, ISCMI 2017
SP - 11
EP - 15
BT - IEEE 4th International Conference on Soft Computing and Machine Intelligence, ISCMI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Soft Computing and Machine Intelligence, ISCMI 2017
Y2 - 23 November 2017 through 24 November 2017
ER -