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Found 14 papers, 0 papers with code
Evaluation of k-means time series clustering based on z-normalization and NP-Free
By systematically investigating the performance of k-means time series clustering with these two normalization techniques, this paper addresses the current gap in k-means time series clustering evaluation and contributes valuable insights to the development of time series clustering.
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series
It is also highly desirable to have a lightweight anomaly detection system that considers correlations between different variables, adapts to changes in the pattern of the multivariate time series, offers immediate responses, and provides supportive information regarding detection results based on unsupervised learning and online model training.
Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection
Randomly choosing a deep learning library to implement an anomaly detection approach might not be able to show the true performance of the approach.
NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series
Without needing to use any normalization method or tune any parameter, NP-Free can generate a representation for a raw time series on the fly by converting each data point of the time series into a root-mean-square error (RMSE) value based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward strategy.
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series
To address this issue, in this paper we propose RePAD2, a lightweight real-time anomaly detection approach for open-ended time series by improving its predecessor RePAD, which is one of the state-of-the-art anomaly detection approaches.
DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks
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
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.
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?
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.
Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing
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
Anomaly detection is an active research topic in many different fields such as intrusion detection, network monitoring, system health monitoring, IoT healthcare, etc.
RePAD: Real-time Proactive Anomaly Detection for Time Series
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.
DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction
Over the past decade, several approaches have been introduced for short-term traffic prediction.
PDS: Deduce Elder Privacy from Smart Homes
To show that elders' privacy could be substantially exposed, in this paper we develop a Privacy Deduction Scheme (PDS for short) by eavesdropping sensor traffic from a smart home to identify elders' movement activities and speculating sensor locations in the smart home based on a series of deductions from the viewpoint of an attacker.
Privacy Mining from IoT-based Smart Homes
The experimental results demonstrate that PMA is able to deduce a global sensor topology for a smart home and disclose elders' privacy in terms of their house layouts.
