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05/23/2023
Deep Learning based Forecasting: a case study from the online fashion industry
Demand forecasting in the online fashion industry is particularly amenda...
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12/07/2022
Criteria for Classifying Forecasting Methods
Classifying forecasting methods as being either of a "machine learning" ...
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09/15/2022
On the detrimental effect of invariances in the likelihood for variational inference
Variational Bayesian posterior inference often requires simplifying appr...
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06/29/2022
Intrinsic Anomaly Detection for Multi-Variate Time Series
We introduce a novel, practically relevant variation of the anomaly dete...
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03/21/2022
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Data-driven methods that detect anomalies in times series data are ubiqu...
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03/16/2022
Resilient Neural Forecasting Systems
Industrial machine learning systems face data challenges that are often ...
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03/07/2022
Multivariate Time Series Forecasting with Latent Graph Inference
This paper introduces a new approach for Multivariate Time Series foreca...
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02/23/2022
Multivariate Quantile Function Forecaster
We propose Multivariate Quantile Function Forecaster (MQF^2), a global p...
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02/17/2022
Multi-Objective Model Selection for Time Series Forecasting
Research on time series forecasting has predominantly focused on develop...
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12/06/2021
Online false discovery rate control for anomaly detection in time series
This article proposes novel rules for false discovery rate control (FDRC...
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11/05/2021
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
While classical time series forecasting considers individual time series...
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10/26/2021
Deep Explicit Duration Switching Models for Time Series
Many complex time series can be effectively subdivided into distinct reg...
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10/25/2021
Neural Flows: Efficient Alternative to Neural ODEs
Neural ordinary differential equations describe how values change in tim...
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09/10/2021
A Study of Joint Graph Inference and Forecasting
We study a recent class of models which uses graph neural networks (GNNs...
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06/08/2021
Detecting Anomalous Event Sequences with Temporal Point Processes
Automatically detecting anomalies in event data can provide substantial ...
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04/08/2021
Neural Temporal Point Processes: A Review
Temporal point processes (TPP) are probabilistic generative models for c...
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10/04/2020
Intermittent Demand Forecasting with Renewal Processes
Intermittency is a common and challenging problem in demand forecasting....
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08/03/2020
A simple and effective predictive resource scaling heuristic for large-scale cloud applications
We propose a simple yet effective policy for the predictive auto-scaling...
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07/30/2020
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
This paper introduces a new methodology for detecting anomalies in time ...
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05/20/2020
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
Time series modeling techniques based on deep learning have seen many ad...
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04/21/2020
Neural forecasting: Introduction and literature overview
Neural network based forecasting methods have become ubiquitous in large...
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11/23/2019
Intermittent Demand Forecasting with Deep Renewal Processes
Intermittent demand, where demand occurrences appear sporadically in tim...
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06/12/2019
GluonTS: Probabilistic Time Series Models in Python
We introduce Gluon Time Series (GluonTS)[<https://gluon-ts.mxnet.io>], a...
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05/28/2019
Deep Factors for Forecasting
Producing probabilistic forecasts for large collections of similar and/o...
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09/22/2017
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
We present a scalable and robust Bayesian inference method for linear st...
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12/27/2010