کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406524 678092 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An improvement of symbolic aggregate approximation distance measure for time series
ترجمه فارسی عنوان
بهبود فاصله تقریبی نمادین برای سری زمانی
کلمات کلیدی
سری زمانی، فاصله روند، سازگاری نمادین، کران پایین، طبقه بندی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• We define an intuitive trend distance measure on time series segments.
• We integrate the SAX distance with our weighted trend distance.
• The proposed distance has a tighter lower bound to Euclidean distance than SAX.
• Our method outperforms the original SAX on classification significantly.

Symbolic Aggregate approXimation (SAX) as a major symbolic representation has been widely used in many time series data mining applications. However, because a symbol is mapped from the average value of a segment, the SAX ignores important information in a segment, namely the trend of the value change in the segment. Such a miss may cause a wrong classification in some cases, since the SAX representation cannot distinguish different time series with similar average values but different trends. In this paper, we firstly design a measure to compute the distance of trends using the starting and the ending points of segments. Then we propose a modified distance measure by integrating the SAX distance with a weighted trend distance. We show that our distance measure has a tighter lower bound to the Euclidean distance than that of the original SAX. The experimental results on diverse time series data sets demonstrate that our proposed representation significantly outperforms the original SAX representation and an improved SAX representation for classification.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 138, 22 August 2014, Pages 189–198
نویسندگان
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