کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
394031 665716 2014 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An approach to dimensionality reduction in time series
ترجمه فارسی عنوان
یک رویکرد به کاهش ابعاد در سری زمانی
کلمات کلیدی
سری داده ها، کاهش ابعاد، گسسته سازی، صفات اسمی، پاکت نامه، ویژگی های ضروری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Many methods of dimensionality reduction of data series (time series) have been introduced over the past decades. Some of them rely on a symbolic representation of the original data, however in this case the obtained dimensionality reduction is not substantial. In this paper, we introduce a new approach referred to as Symbolic Essential Attributes Approximation (SEAA) to reduce the dimensionality of multidimensional time series. In such a way we form a new nominal representation of the original data series. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network. The real-valued attributes are discretized, and in this way symbolic data series representation is formed. The SEAA generates a vector of nominal values of new attributes which form the compressed representation of original data series. The nominal attributes are synthetic, and while not being directly interpretable, they still retain important features of the original data series. A validation of usefulness of the proposed dimensionality reduction is carried out for classification and clustering tasks. The experiments have shown that even for a significant reduction of dimensionality, the new representation retains information about the data series sufficient for classification and clustering of the time series.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 260, 1 March 2014, Pages 15–36
نویسندگان
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