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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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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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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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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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07/16/2021
Neural Contextual Anomaly Detection for Time Series
We introduce Neural Contextual Anomaly Detection (NCAD), a framework for...
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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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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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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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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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04/13/2017