Search Results for author: Jan Gasthaus

Found 27 papers, 7 papers with code

Criteria for Classifying Forecasting Methods

no code implementations7 Dec 2022 Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot

Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers.

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 +3

Multivariate Quantile Function Forecaster

no code implementations23 Feb 2022 Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus

We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting.

Online Time Series Anomaly Detection with State Space Gaussian Processes

no code implementations18 Jan 2022 Christian Bock, François-Xavier Aubet, Jan Gasthaus, Andrey Kan, Ming Chen, Laurent Callot

We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes.

Anomaly Detection Gaussian Processes +2

Monte Carlo EM for Deep Time Series Anomaly Detection

1 code implementation29 Dec 2021 François-Xavier Aubet, Daniel Zügner, Jan Gasthaus

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).

Anomaly Detection Time Series +1

Probabilistic Forecasting: A Level-Set Approach

no code implementations NeurIPS 2021 Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus

By recognizing the connection of our algorithm to random forests (RFs) and quantile regression forests (QRFs), we are able to prove consistency guarantees of our approach under mild assumptions on the underlying point estimator.

quantile regression Time Series +1

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting

no code implementations12 Nov 2021 Youngsuk Park, Danielle Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang

Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint learnings over multiple quantile levels.

quantile regression Time Series +1

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 +2

A Study of Joint Graph Inference and Forecasting

no code implementations10 Sep 2021 Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus

We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series.

Graph Learning Time Series +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 +2

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting

no code implementations NeurIPS 2020 Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus

We propose a Monte Carlo objective that leverages the conditional linearity by computing the corresponding conditional expectations in closed-form and a suitable proposal distribution that is factorised similarly to the optimal proposal distribution.

Decoder Time Series +1

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

no code implementations30 Jul 2020 Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus

Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series.

Anomaly Detection Time Series +1

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 Time Series Analysis

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

2 code implementations NeurIPS 2019 David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus

Predicting the dependencies between observations from multiple time series is critical for applications such as anomaly detection, financial risk management, causal analysis, or demand forecasting.

Anomaly Detection Management +3

Deep Factors for Forecasting

no code implementations28 May 2019 Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski

We provide both theoretical and empirical evidence for the soundness of our approach through a necessary and sufficient decomposition of exchangeable time series into a global and a local part.

Time Series Time Series Analysis

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

19 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 +1

GP-select: Accelerating EM using adaptive subspace preselection

no code implementations10 Dec 2014 Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Joerg Luecke, Arthur Gretton

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large.

Object Localization

Improvements to the Sequence Memoizer

no code implementations NeurIPS 2010 Jan Gasthaus, Yee W. Teh

The sequence memoizer is a model for sequence data with state-of-the-art performance on language modeling and compression.

Language Modelling

Dependent Dirichlet Process Spike Sorting

no code implementations NeurIPS 2008 Jan Gasthaus, Frank Wood, Dilan Gorur, Yee W. Teh

In this paper we propose a new incremental spike sorting model that automatically eliminates refractory period violations, accounts for action potential waveform drift, and can handle appearance" and "disappearance" of neurons.

Spike Sorting

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