Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Monte Carlo EM for Deep Time Series Anomaly Detection   Time series data are often corrupted by outliers or other kinds of anomalies.
Identifying the anomalous points can be a goal on its own (anomaly detection),
or a means to improving performance of other time series tasks (e.g.
forecasting). Recent deep-learning-based approaches to anomaly detection and
forecasting commonly assume that the proportion of anomalies in the training
data is small enough to ignore, and treat the unlabeled data as coming from the
nominal data distribution. We present a simple yet effective technique for
augmenting existing time series models so that they explicitly account for
anomalies in the training data. By augmenting the training data with a latent
anomaly indicator variable whose distribution is inferred while training the
underlying model using Monte Carlo EM, our method simultaneously infers
anomalous points while improving model performance on nominal data. We
demonstrate the effectiveness of the approach by combining it with a simple
feed-forward forecasting model. We investigate how anomalies in the train set
affect the training of forecasting models, which are commonly used for time
series anomaly detection, and show that our method improves the training of the
model.

19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci