Search Results for author: Tim Januschowski

Found 25 papers, 5 papers with code

Resilient Neural Forecasting Systems

no code implementations16 Mar 2022 Michael Bohlke-Schneider, Shubham Kapoor, Tim Januschowski

Common data challenges are data distribution shifts, missing values and anomalies.

Imputation

Multivariate Time Series Forecasting with Latent Graph Inference

no code implementations7 Mar 2022 Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski

This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series.

Multivariate Time Series Forecasting Time Series

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.

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 +1

Online false discovery rate control for anomaly detection in time series

no code implementations NeurIPS 2021 Quentin Rebjock, Barış Kurt, Tim Januschowski, Laurent Callot

The methods proposed in this article overcome short-comings of previous FDRC rules in the context of anomaly detection, in particular ensuring that power remains high even when the alternative is exceedingly rare (typical in anomaly detection) and the test statistics are serially dependent (typical in time series).

Anomaly Detection Time Series

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.

Time Series

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 +1

Deep Explicit Duration Switching Models for Time Series

1 code implementation NeurIPS 2021 Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J. Smola, Yuyang Wang, Tim Januschowski

We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics.

Time Series

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

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

Detecting Anomalous Event Sequences with Temporal Point Processes

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.

Frame OOD Detection +1

Neural Temporal Point Processes: A Review

no code implementations8 Apr 2021 Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann

Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences.

Point Processes

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.

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

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

Intermittent Demand Forecasting with Deep Renewal Processes

1 code implementation23 Nov 2019 Ali Caner Turkmen, Yuyang Wang, Tim Januschowski

Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting.

Point Processes

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

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