1.4k citations · 1.9k across the 28 of their papers we have counts for
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Ensembles of Localised Models for Time Series Forecasting
Rakshitha Godahewa, Kasun Bandara, Geoffrey I. Webb +2
With large quantities of data typically available nowadays, forecasting models that are trained across sets of time series, known as Global Forecasting Models (GFM), are regularly…
Global Models for Time Series Forecasting: A Simulation Study
Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
In the current context of Big Data, the nature of many forecasting problems has changed from predicting isolated time series to predicting many time series from similar sources. Th…
Forecasting: theory and practice
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos +77
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organ…
Model selection in reconciling hierarchical time series
Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis +1
Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart fro…
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +1
Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, re…
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu +2
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and…
Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification
Rakshitha Godahewa, Trevor Yann, Christoph Bergmeir +1
Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal d…
Simulation and Optimisation of Air Conditioning Systems using Machine Learning
Rakshitha Godahewa, Chang Deng, Arnaud Prouzeau +1
In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy. This strate…
Time Series Extrinsic Regression
Chang Wei Tan, Christoph Bergmeir, Francois Petitjean +1
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable;…
Monash University, UEA, UCR Time Series Extrinsic Regression Archive
Chang Wei Tan, Christoph Bergmeir, Francois Petitjean +1
Time series research has gathered lots of interests in the last decade, especially for Time Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has great…
Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand
Kasun Bandara, Christoph Bergmeir, Sam Campbell +2
Emergency Medical Services (EMS) demand load has become a considerable burden for many government authorities, and EMS demand is often an early indicator for stress in communities,…