Time Series Classification with Linguistic Summaries
Katarzyna Kaczmarek, Olgierd Hryniewicz
Available Online June 2015.
- https://doi.org/10.2991/ifsa-eusflat-15.2015.68How to use a DOI?
- Time series analysis, classification, linguistic summaries.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.68 ID - Kaczmarek2015/06 ER -