@inproceedings {10.1007/978-3-030-57675-2_15,
	title = {Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks Based on Automatic LSTM Customization and Sharing},
	journal = {Euro-Par 2020: 26th International Conference on Parallel and Distributed Computing},
	year = {2020},
	month = {08/2020},
	pages = {234{\textendash}247},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {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, and efficient traffic-speed prediction for large-scale transportation networks where numerous traffic detectors are deployed has not been well studied. In this paper, we propose DistPre, which is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. To achieve fine-grained and accurate traffic-speed prediction, DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate hyperparameter configuration for a detector. To make such a 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. If a detector observes a similar traffic pattern to another one, DistPre directly shares the existing LSTM model between the two detectors rather than customizing an LSTM model per detector. Experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistPre. The results demonstrate that DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.},
	isbn = {978-3-030-57675-2},
	doi = {10.1007/978-3-030-57675-2_15},
	author = {Lee, Ming-Chang and Lin, Jia-Chun and Ernst Gunnar {Gran}},
	editor = {Malawski, Maciej and Rzadca, Krzysztof}
}
@inproceedings {10.1007/978-3-030-44041-1_110,
	title = {RePAD: Real-Time Proactive Anomaly Detection for Time Series},
	journal = {Advanced Information Networking and Applications},
	year = {2020},
	month = {03/2020},
	pages = {1291{\textendash}1302},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. 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. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historical data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.},
	isbn = {978-3-030-44041-1},
	doi = {10.1007/978-3-030-44041-1_110},
	author = {Lee, Ming-Chang and Lin, Jia-Chun and Ernst Gunnar {Gran}},
	editor = {Barolli, Leonard and Amato, Flora and Moscato, Francesco and Enokido, Tomoya and Takizawa, Makoto}
}
@inproceedings {27415,
	title = {ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series},
	journal = {IEEE 44th Annual Computers, Software, and Applications Conference },
	year = {2020},
	pages = {322-327},
	publisher = {IEEE},
	abstract = {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 existing anomaly detection approaches require either human intervention or domain knowledge, and may suffer from high computation complexity, consequently hindering their applicability in real-world scenarios. Therefore, a lightweight and ready-to-go approach that is able to detect anomalies in real-time is highly sought-after. Such an approach could be easily and immediately applied to perform time series anomaly detection on any commodity machine. The approach could provide timely anomaly alerts and by that enable appropriate countermeasures to be undertaken as early as possible. With these goals in mind, this paper introduces ReRe, which is a Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to predict and jointly determine whether or not an upcoming data point is anomalous based on short-term historical data points and two long-term self-adaptive thresholds. Experiments based on real-world time-series datasets demonstrate the good performance of ReRe in real-time anomaly detection without requiring human intervention or domain knowledge.},
	isbn = {978-1-7281-7303-0},
	issn = {0730-3157},
	doi = {10.1109/COMPSAC48688.2020.0-226},
	author = {Lee, Ming-Chang and Lin, Jia-Chun and Ernst Gunnar {Gran}},
	editor = {O{\textquoteright}Conner, Lisa}
}
