1 citations · 1 across the 6 of their papers we have counts for
6 papers
Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection
Ming-Chang Lee, Jia-Chun Lin, Sokratis Katsikas
Real-time lightweight time series anomaly detection has become increasingly crucial in cybersecurity and many other domains. Its ability to adapt to unforeseen pattern changes and…
Evaluation of k-means time series clustering based on z-normalization and NP-Free
Ming-Chang Lee, Jia-Chun Lin, Volker Stolz
Despite the widespread use of k-means time series clustering in various domains, there exists a gap in the literature regarding its comprehensive evaluation with different time ser…
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series
Ming-Chang Lee, Jia-Chun Lin
A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivar…
Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection
Ming-Chang Lee, Jia-Chun Lin
Providing online adaptive lightweight time series anomaly detection without human intervention and domain knowledge is highly valuable. Several such anomaly detection approaches ha…
NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series
Ming-Chang Lee, Jia-Chun Lin, Volker Stolz
As more connected devices are implemented in a cyber-physical world and data is expected to be collected and processed in real time, the ability to handle time series data has beco…
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series
Ming-Chang Lee, Jia-Chun Lin
An open-ended time series refers to a series of data points indexed in time order without an end. Such a time series can be found everywhere due to the prevalence of Internet of Th…