Efficient Mining of Weighted Flexible Periodic Patterns in Time Series Databases using a Period-based Algorithm with Multiple Join Policies

Periodic pattern mining in time series databases is crucial for predicting future trends. The L-PPM algorithm is a list structure of the flexible periodic pattern that can increase the flexibility of the mining result. However, it will generate an invalid candidate, like the pattern with the last sy...

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Bibliographic Details
Published inInternational Conference on Applied System Innovation (Online) pp. 351 - 353
Main Authors Chang, Ye-In, Li, Chia-En, Chiang, Sheng-Hsin
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.04.2024
Subjects
Bidirectional control
flexible periodic pattern
Noise
Parallel processing
periodic pattern mining
Symbols
Technological innovation
Time series analysis
time series databases
Online AccessGet full text
ISSN2768-4156
DOI10.1109/ICASI60819.2024.10547739

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Summary:Periodic pattern mining in time series databases is crucial for predicting future trends. The L-PPM algorithm is a list structure of the flexible periodic pattern that can increase the flexibility of the mining result. However, it will generate an invalid candidate, like the pattern with the last symbol = '*'. To solve this, we propose a Period-based algorithm with a PDH table. It allows for parallel processing and avoids invalid candidates. Performance studies show its efficiency over the L-PPM algorithm.
ISSN:2768-4156
DOI:10.1109/ICASI60819.2024.10547739