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.
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.
no code implementations • 10 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.
19 code implementations • 13 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
no code implementations • 22 Sep 2017 • Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
We present a scalable and robust Bayesian inference method for linear state space models.
2 code implementations • NeurIPS 2018 • Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.
no code implementations • 28 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.
6 code implementations • 12 Jun 2019 • Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
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.
1 code implementation • 21 Apr 2020 • Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Francois-Xavier Aubet, Laurent Callot, Tim Januschowski
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches.
no code implementations • 20 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.
no code implementations • 30 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.
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.
no code implementations • NeurIPS 2021 • Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security.
1 code implementation • 16 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.
no code implementations • 10 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.
no code implementations • 29 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.
no code implementations • 12 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.
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.
1 code implementation • 29 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).
no code implementations • 18 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.
no code implementations • 23 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.
no code implementations • 21 Mar 2022 • Deborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cedric Archambeau
In this work we propose a model-agnostic algorithm that generates counterfactual ensemble explanations for time series anomaly detection models.
no code implementations • 29 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.
no code implementations • 15 Sep 2022 • Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
Then, we transfer our analysis of the linear model to neural networks.
no code implementations • 7 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.
no code implementations • 22 Dec 2023 • Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
This paper presents non-parametric baseline models for time series forecasting.