Publications (21)
Monte Carlo EM for Deep Time Series Anomaly Detection
François-Xavier Aubet, Daniel Zügner, Jan Gasthaus
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…
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
Stephan Rabanser, Tim Januschowski, Valentin Flunkert +2
Time series modeling techniques based on deep learning have seen many advancements in recent years, especially in data-abundant settings and with the central aim of learning global…
Intrinsic Anomaly Detection for Multi-Variate Time Series
Stephan Rabanser, Tim Januschowski, Kashif Rasul +6
We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection. It appears in diverse practical sce…
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
Matthias Seeger, Syama Rangapuram, Yuyang Wang +4
We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform,…
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Fadhel Ayed, Lorenzo Stella, Tim Januschowski +1
This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. T…
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
David Salinas, Valentin Flunkert, Jan Gasthaus
Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail busin…