368 citations · 635 across the 21 of their papers we have counts for
17 papers · 1 filter
Deep Non-Parametric Time Series Forecaster
Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella +4
This paper presents non-parametric baseline models for time series forecasting. Unlike classical forecasting models, the proposed approach does not assume any parametric form for t…
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle, Ralf Herbrich, Tim Januschowski +2
Variational Bayesian posterior inference often requires simplifying approximations such as mean-field parametrisation to ensure tractability. However, prior work has associated the…
Intrinsic Anomaly Detection for Multi-Variate Time Series
Stephan Rabanser, Tim Januschowski, Kashif Rasul +6
We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection. It appears in diverse practical sce…
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they m…
Multivariate Quantile Function Forecaster
Kelvin Kan, François-Xavier Aubet, Tim Januschowski +4
We propose Multivariate Quantile Function Forecaster (MQF), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its app…
Online Time Series Anomaly Detection with State Space Gaussian Processes
Christian Bock, François-Xavier Aubet, Jan Gasthaus +3
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. F…
Monte Carlo EM for Deep Time Series Anomaly Detection
François-Xavier Aubet, Daniel Zügner, Jan Gasthaus
Time series data are often corrupted by outliers or other kinds of anomalies. Identifying the anomalous points can be a goal on its own (anomaly detection), or a means to improving…
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Youngsuk Park, Danielle Maddix, François-Xavier Aubet +3
Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint lear…
A Study of Joint Graph Inference and Forecasting
Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras +3
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series. The core assumption behind these models is that there…
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
Jeha Paul, Bohlke-Schneider Michael, Mercado Pedro +5
Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper…
Neural Contextual Anomaly Detection for Time Series
Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert +1
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…
Detecting Anomalous Event Sequences with Temporal Point Processes
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +2
Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem o…
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Fadhel Ayed, Lorenzo Stella, Tim Januschowski +1
This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. T…
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
Stephan Rabanser, Tim Januschowski, Valentin Flunkert +2
Time series modeling techniques based on deep learning have seen many advancements in recent years, especially in data-abundant settings and with the central aim of learning global…
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert +10
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Conseq…
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
David Salinas, Michael Bohlke-Schneider, Laurent Callot +2
Predicting the dependencies between observations from multiple time series is critical for applications such as anomaly detection, financial risk management, causal analysis, or de…
GluonTS: Probabilistic Time Series Models in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider +10
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and…