Knowing when one does not know is crucial in decision making. By estimating uncertainties humans can recognize novelty both by intuition and reason, but most AI systems lack this self-reflective ability. In anomaly detection, a common approach is to train a model to learn the distinction between some notion of normal and some notion of anomalies. In contrast, we let the models build their own notion of normal by learning directly from the data in a self-supervised manner, and by introducing estimations of model uncertainty the models can recognize themselves when novel situations are encountered. In our work, the aim is to investigate the relationship between model uncertainty and anomalies in time series data. We develop a method based on a recurrent autoencoder approach, and we design an anomaly score function that aggregates model error with model uncertainty to indicate anomalies. Use the Monte Carlo Dropout as Bayesian approximation to derive model uncertainty. Asa proof of concept we evaluate our method qualitatively on real-world complex time series using stock market data. Results show that our method can identify extreme events in the stock market. We conclude that the relation between model uncertainty and anomalies can be utilized for anomaly detection in time series data.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-199879 |
| Date | January 2022 |
| Creators | Vidmark, Anton |
| Publisher | Umeå universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UMNAD ; 1363 |
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