سال انتشار:
2020
عنوان انگلیسی مقاله:
Policy-based reinforcement learning for time series anomaly detection
ترجمه فارسی عنوان مقاله:
یادگیری تقویتی مبتنی بر سیاست برای تشخیص ناهنجاری سری زمانی
منبع:
Sciencedirect - Elsevier - Engineering Applications of Artificial Intelligence, 95 (2020) 103919. doi:10.1016/j.engappai.2020.103919
نویسنده:
Mengran Yu, Shiliang Sun ∗
چکیده انگلیسی:
Time series anomaly detection has become a crucial and challenging task driven by the rapid increase
of streaming data with the arrival of the Internet of Things. Existing methods are either domain-specific
or require strong assumptions that cannot be met in realistic datasets. Reinforcement learning (RL), as an
incremental self-learning approach, could avoid the two issues well. However, the current investigation is far
from comprehensive. In this paper, we propose a generic policy-based RL framework to address the time series
anomaly detection problem. The policy-based time series anomaly detector (PTAD) is progressively learned
from the interactions with time-series data in the absence of constraints. Experimental results show that it
outperforms the value-based temporal anomaly detector and other state-of-the-art detection methods whether
training and test datasets come from the same source or not. Furthermore, the tradeoff between precision and
recall is well respected by the PTAD, which is beneficial to fulfill various industrial requirements.
Keywords: Time series anomaly detection | Reinforcement learning | Policy-based methods
قیمت: رایگان
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