A Data-Distillation-Enhanced Autoencoder for Detecting Anomalous Gas Consumption

Zhou, Yujue; Jiang, Jie; Yang, Shuang–Hua; He, Ligang; Ding, Yulong; Liu, Kai; Zhu, Guozhong; Qing, Ying

doi:10.1109/jiot.2023.3296538

IEEE Internet Things J.

2024

DOI: 10.1109/jiot.2023.3296538

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A Data-Distillation-Enhanced Autoencoder for Detecting Anomalous Gas Consumption

Yujue Zhou

Jie Jiang

Shuang–Hua Yang

et al.

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

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Sampling approaches to reduce very frequent seasonal time series

Baldo,

Ferreira,

Mendes‐Moreira

2024

Expert Systems

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With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data‐driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time‐consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time‐series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt‐Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed.

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Sampling approaches to reduce very frequent seasonal time series

Baldo,

Ferreira,

Mendes‐Moreira

2024

Expert Systems

View full text Add to dashboard Cite

show abstract

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A Data-Distillation-Enhanced Autoencoder for Detecting Anomalous Gas Consumption

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 1 publication

References 36 publications

Sampling approaches to reduce very frequent seasonal time series

Sampling approaches to reduce very frequent seasonal time series

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