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TLEX: A Temporal Analysis Tool for Time Series Data

Authors:
Mohammed AL Zamil
Bilal Abu AL Huda

Keywords: Temporal Data Mining; Classification of Temporal Data; Lexical Patterns

Abstract:
Time is an essential dimension to many domain specific problems, such as the medical and financial domains. This research introduces TLEX(Temporal Lexical Patterns),a framework to categorize temporal data that effectively induces semantic temporal patterns. TLEX is a rule-based classification framework dedicated to enhance the classification accuracy by focusing on eliminating outliers and minimizing classification errors. The contributions of this research are 1) formulating semantic temporal patterns as basic classification features, and 2) introducing an induction technique to discriminate semantic temporal patterns. To illustrate the design, the paper provides a detailed mathematical description that relies on set-theory to model the framework of TLEX. Furthermore, a detailed description of the proposed algorithms to facilitate implementing and reproducing the results has been described. Further, to evaluate the effectiveness of TLEX, extensive experiments have been performed on a weather temporal dataset. Accordingly, the F-measure and support values on weather dataset have been reported. Further, a sensitivity analysis to assess the capability of TLEX to work with temporal datasets has been provided. The findings indicate a significant improvement of Temporal-ROLEX over some existing techniques.

Pages: 30 to 34

Copyright: Copyright (c) IARIA, 2017

Publication date: April 23, 2017

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-552-4

Location: Venice, Italy

Dates: from April 23, 2017 to April 27, 2017