| Autor: |
Neema Davis, Gaurav Raina, Krishna Jagannathan |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Transportation Research Interdisciplinary Perspectives, Vol 5, Iss , Pp 100112- (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1982 |
| DOI: |
10.1016/j.trip.2020.100112 |
| Popis: |
We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
