Publikationer pr. år
Publikationer pr. år
Tung Kieu*, Bin Yang, Christian Søndergaard Jensen
Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review
Due to the continued digitization of industrial and societal processes, including the deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered observations, known as time series. For example, the behavior of drivers can be captured by GPS or accelerometer as a time series of speeds, directions, and accelerations. We propose a framework for outlier detection in time series that, for example, can be used for identifying dangerous driving behavior and hazardous road locations. Specifically, we first propose a method that generates statistical features to enrich the feature space of raw time series. Next, we utilize an autoencoder to reconstruct the enriched time series. The autoencoder performs dimensionality reduction to capture, using a small feature space, the most representative features of the enriched time series. As a result, the reconstructed time series only capture representative features, whereas outliers often have non-representative features. Therefore, deviations of the enriched time series from the reconstructed time series can be taken as indicators of outliers. We propose and study autoencoders based on convolutional neural networks and long-short term memory neural networks. In addition, we show that embedding of contextual information into the framework has the potential to further improve the accuracy of identifying outliers. We report on empirical studies with multiple time series data sets, which offers insight into the design properties of the proposed framework, indicating that it is effective at detecting outliers.
Originalsprog | Engelsk |
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Titel | Proceedings of the 19th IEEE International Conference on Mobile Data Management, MDM 2018 |
Antal sider | 10 |
Vol/bind | 2018-June |
Forlag | IEEE |
Publikationsdato | 13 jul. 2018 |
Sider | 125-134 |
ISBN (Elektronisk) | 9781538641330 |
DOI | |
Status | Udgivet - 13 jul. 2018 |
Begivenhed | 19th IEEE International Conference on Mobile Data Management, MDM 2018 - Aalborg, Danmark Varighed: 25 jun. 2018 → 28 jun. 2018 |
Konference | 19th IEEE International Conference on Mobile Data Management, MDM 2018 |
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Land/Område | Danmark |
By | Aalborg |
Periode | 25/06/2018 → 28/06/2018 |
Sponsor | Aalborg University, Center for Data-Intensive Systems (DAISY), Aalborg University, IEEE, IEEE Technical Committee on Data Engineering (TCDE), Otto Monsted Foundation |
Navn | IEEE International Conference on Mobile Data Management (MDM) |
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ISSN | 2375-0324 |
Publikation: Ph.d.-afhandling