Search Results for author: Ernst Gunnar Gran

Found 6 papers, 0 papers with code

DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks

no code implementations19 May 2021 Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran

Otherwise, DistTune customizes an LSTM model for the detector to achieve fine-grained prediction.

SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series

no code implementations19 Apr 2021 Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran

If the difference between a calculated AARE value and its corresponding forecast AARE value is higher than a self-adaptive detection threshold, the corresponding data point is considered anomalous.

Anomaly Detection Time Series +1

How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?

no code implementations12 Feb 2021 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 detection, healthcare, intrusion detection, etc.

Anomaly Detection Fraud Detection +3

Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing

no code implementations10 May 2020 Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran

To make such customization process efficient and applicable for large-scale transportation networks, DistPre conducts LSTM customization on a cluster of computation nodes and allows any trained LSTM model to be shared between different detectors.

ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series

no code implementations5 Apr 2020 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.

Anomaly Detection Intrusion Detection +2

RePAD: Real-time Proactive Anomaly Detection for Time Series

no code implementations24 Jan 2020 Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran

Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs.

Anomaly Detection Fraud Detection +3

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