# Monte Carlo EM for Deep Time Series Anomaly Detection

arXiv: https://arxiv.org/abs/2112.14436 · HTML: https://mlsanity.com/paper/2112.14436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Neural Contextual Anomaly Detection for Time Series](https://mlsanity.com/paper/2107.07702/) (2021)
- [Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data](https://mlsanity.com/paper/1708.03665/) (2017)
- [Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series](https://mlsanity.com/paper/2106.10952/) (2021)

## Built on by (most cited first, 1 of 1)

- [Online Time Series Anomaly Detection with State Space Gaussian Processes](https://mlsanity.com/paper/2201.06763/) (2022)

## Abstract (arXiv)

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.