Jan Gasthaus

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Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models

This paper introduces a new methodology for detecting anomalies in time ...
07/30/2020 ∙ by Fadhel Ayed, et al. ∙ 0 ∙ share

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The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models

Time series modeling techniques based on deep learning have seen many ad...
05/20/2020 ∙ by Stephan Rabanser, et al. ∙ 6 ∙ share

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Neural forecasting: Introduction and literature overview

Neural network based forecasting methods have become ubiquitous in large...
04/21/2020 ∙ by Konstantinos Benidis, et al. ∙ 46 ∙ share

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High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes

Predicting the dependencies between observations from multiple time seri...
10/07/2019 ∙ by David Salinas, et al. ∙ 0 ∙ share

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GluonTS: Probabilistic Time Series Models in Python

We introduce Gluon Time Series (GluonTS)[<https://gluon-ts.mxnet.io>], a...
06/12/2019 ∙ by Alexander Alexandrov, et al. ∙ 0 ∙ share

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Deep Factors for Forecasting

Producing probabilistic forecasts for large collections of similar and/o...
05/28/2019 ∙ by Yuyang Wang, et al. ∙ 6 ∙ share

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Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

We present a scalable and robust Bayesian inference method for linear st...
09/22/2017 ∙ by Matthias Seeger, et al. ∙ 0 ∙ share

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DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

A key enabler for optimizing business processes is accurately estimating...
04/13/2017 ∙ by Valentin Flunkert, et al. ∙ 0 ∙ share

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GP-select: Accelerating EM using adaptive subspace preselection

We propose a nonparametric procedure to achieve fast inference in genera...
12/10/2014 ∙ by Jacquelyn A. Shelton, et al. ∙ 0 ∙ share

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Jan Gasthaus

Featured Co-authors

Arthur Gretton

Alex Smola

Zhenwen Dai

Yuyang Wang

Matthias Seeger

Tim Januschowski

Fadhel Ayed

David Salinas

Dean Foster

Valentin Flunkert

Syama Sundar Rangapuram

Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models

Neural forecasting: Introduction and literature overview

High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes

GluonTS: Probabilistic Time Series Models in Python

Deep Factors for Forecasting

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

GP-select: Accelerating EM using adaptive subspace preselection