دانلود مقاله ISI انگلیسی شماره 159947
ترجمه فارسی عنوان مقاله

روش حافظه زمانی سلسله مراتبی برای تشخیص آنومالی مبتنی بر سری زمانی

عنوان انگلیسی
Hierarchical Temporal Memory method for time-series-based anomaly detection
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
159947 2018 35 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Neurocomputing, Volume 273, 17 January 2018, Pages 535-546

ترجمه کلمات کلیدی
تشخیص آنومالی، شبکه عصبی بیولوژیک، حافظه زمانی سلسله مراتبی،
کلمات کلیدی انگلیسی
Anomaly detection; Biological neural network; Hierarchical Temporal Memory;
پیش نمایش مقاله
پیش نمایش مقاله  روش حافظه زمانی سلسله مراتبی برای تشخیص آنومالی مبتنی بر سری زمانی

چکیده انگلیسی

The time-series-based anomaly detection is a well-studied subject, and it is well-documented in the literature. Theories and techniques have been proposed and applied successfully for domain-specific applications. However, this subject has received renewed interest motivated by the increasing importance of continuously learning, tolerance to noise and generalization. This paper tackles these problems by applying Hierarchical Temporal Memory (HTM), a novel biological neural network. HTM is more suitable for dealing with the changing pattern of data since it is capable of incorporating contextual information from the past to make more accurate prediction. Both artificial and real datasets are tested with HTM for the time-series-based anomaly detection. The experiment results show that HTM can efficiently detect the anomalies in time series data.