56 citations · 96 across the 11 of their papers we have counts for
11 papers
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
Direnc Pekaslan, Jose Maria Alonso-Moral, Kasun Bandara +17
This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key co…
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach
Xueying Long, Quang Bui, Grady Oktavian +6
The recent M5 competition has advanced the state-of-the-art in retail forecasting. However, we notice important differences between the competition challenge and the challenges we…
On Forecast Stability
Rakshitha Godahewa, Christoph Bergmeir, Zeynep Erkin Baz +4
Forecasts are typically not produced in a vacuum but in a business context, where forecasts are generated on a regular basis and interact with each other. For decisions, it may be…
Handling Concept Drift in Global Time Series Forecasting
Ziyi Liu, Rakshitha Godahewa, Kasun Bandara +1
Machine learning (ML) based time series forecasting models often require and assume certain degrees of stationarity in the data when producing forecasts. However, in many real-worl…
Predict+Optimize Problem in Renewable Energy Scheduling
Christoph Bergmeir, Frits de Nijs, Evgenii Genov +25
Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are criti…
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
Rakshitha Godahewa, Geoffrey I. Webb, Daniel Schmidt +1
Threshold Autoregressive (TAR) models have been widely used by statisticians for non-linear time series forecasting during the past few decades, due to their simplicity and mathema…
Monash Time Series Forecasting Archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +2
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are t…
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…
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…
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…