کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11003607 1461457 2018 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Scale-varying dynamic time warping based on hesitant fuzzy sets for multivariate time series classification
ترجمه فارسی عنوان
انحراف زمان پویا در مقیاس مختلف بر اساس مجموعه های فازی هنجار برای طبقه بندی سری چند متغیره
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
چکیده انگلیسی
Multivariate time series (MTS) data widely exists in daily life. How to classify MTS remains a major problem in data mining, computer science, financial area and other relative industry. MTS data is always treated as a whole object or time instance one by one, while in this paper, time instance segments were paid more attention. A new distance measure named dynamic time warping based on hesitant fuzzy sets (HFS-DTW) is proposed for MTS classification. HFS-DTW is a generalized dynamic time warping algorithm, and due to the characteristic of HFS, it is easy to find optimal alignment between time instance segments. Also, the proposed method could be reduced to original DTW by setting scale parameters. In order to apply the proposed algorithm correctly and efficiently, the parameter constraints were discussed. Furthermore, using 10-fold cross-validation, five MTS data sets selected from the University of California, Irvine machine learning repository, were tested by the proposed algorithm. By comparing with state-of-the-art algorithms, the results demonstrate the proposed method could balance the higher accuracy and lower time-consuming in classification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 130, December 2018, Pages 290-297
نویسندگان
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