Search Results for author: Valentin Flunkert

Found 12 papers, 3 papers with code

Intrinsic Anomaly Detection for Multi-Variate Time Series

no code implementations29 Jun 2022 Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert

We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection.

Anomaly Detection Navigate +2

Multi-Objective Model Selection for Time Series Forecasting

no code implementations17 Feb 2022 Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann

By learning a mapping from forecasting models to performance metrics, we show that our method PARETOSELECT is able to accurately select models from the Pareto front -- alleviating the need to train or evaluate many forecasting models for model selection.

Model Selection Time Series Forecasting

Meta-Forecasting by combining Global Deep Representations with Local Adaptation

no code implementations5 Nov 2021 Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias Seeger, Cedric Archambeau

While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy.

Meta-Learning Time Series Forecasting

Context-invariant, multi-variate time series representations

no code implementations29 Sep 2021 Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert

Modern time series corpora, in particular those coming from sensor-based data, exhibit characteristics that have so far not been adequately addressed in the literature on representation learning for time series.

Contrastive Learning Representation Learning +1

Neural Contextual Anomaly Detection for Time Series

1 code implementation16 Jul 2021 Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, Jan Gasthaus

We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series.

Contextual Anomaly Detection Representation Learning +1

A simple and effective predictive resource scaling heuristic for large-scale cloud applications

no code implementations3 Aug 2020 Valentin Flunkert, Quentin Rebjock, Joel Castellon, Laurent Callot, Tim Januschowski

We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be added with a delay, and where the deployment throughput is limited.

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

no code implementations20 May 2020 Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus

In particular, we investigate the effectiveness of several forms of data binning, i. e. converting real-valued time series into categorical ones, when combined with feed-forward, recurrent neural networks, and convolution-based sequence models.

Time Series

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

14 code implementations13 Apr 2017 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.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting

Bayesian Intermittent Demand Forecasting for Large Inventories

no code implementations NeurIPS 2016 Matthias W. Seeger, David Salinas, Valentin Flunkert

We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics.

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