74 citations · 192 across the 17 of their papers we have counts for
12 papers · 1 filter
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…
DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks
Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
Over the past decade, many approaches have been introduced for traffic speed prediction. However, providing fine-grained, accurate, time-efficient, and adaptive traffic speed predi…
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series
Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
Real-world time series data often present recurrent or repetitive patterns and it is often generated in real time, such as transportation passenger volume, network traffic, system…
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?
Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
Anomaly detection is the process of identifying unexpected events or ab-normalities in data, and it has been applied in many different areas such as system monitoring, fraud detect…
Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing
Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, an…
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series
Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
Anomaly detection is an active research topic in many different fields such as intrusion detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many…
DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction
Ming-Chang Lee, Jia-Chun Lin
Over the past decade, several approaches have been introduced for short-term traffic prediction. However, providing fine-grained traffic prediction for large-scale transportation n…
RePAD: Real-time Proactive Anomaly Detection for Time Series
Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, th…
Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress
Ming-Chang Lee, Chang To
Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back propa…