Time Series Classification with Linguistic Summaries

DOI

10.2991/ifsa-eusflat-15.2015.68How to use a DOI?

Keywords

Time series analysis, classification, linguistic summaries.

Abstract

Soft computing techniques may provide various forms of human-consistent summaries about large time series databases, e.g., linguistic summaries, frequent patterns, fuzzy IF-THEN rules. Within this research, we focus on linguistic summaries constructed as linguistically quantified propositions, that may be exemplified by ‘Among all increasing trends, most are short’. We pose the question whether such imprecise results of summarization may successfully support the classification of time series data. Within the proposed approach, we classify a vector of linguistic ummaries instead of classifying crisp time series. The approach is illustrated with experiments on artificial and benchmark real-life time series datasets. It turns out to be very promising for the classification of autoregressive time series by the probabilistic models.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Katarzyna Kaczmarek
AU  - Olgierd Hryniewicz
PY  - 2015/06
DA  - 2015/06
TI  - Time Series Classification with Linguistic Summaries
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 471
EP  - 477
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.68
DO  - 10.2991/ifsa-eusflat-15.2015.68
ID  - Kaczmarek2015/06
ER  -