1.4k citations · 1.7k across the 12 of their papers we have counts for
14 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…
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…
A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study
Saed Alizamir, Kasun Bandara, Ali Eshragh +1
We apply classical statistical methods in conjunction with the state-of-the-art machine learning techniques to develop a hybrid interpretable model to analyse 454,897 online custom…
Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations
Maneesha Perera, Julian De Hoog, Kasun Bandara +1
Distributed, small-scale solar photovoltaic (PV) systems are being installed at a rapidly increasing rate. This can cause major impacts on distribution networks and energy markets.…
MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns
Kasun Bandara, Rob J Hyndman, Christoph Bergmeir
The decomposition of time series into components is an important task that helps to understand time series and can enable better forecasting. Nowadays, with high sampling rates lea…
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…
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…
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,…
The Importance of Environmental Factors in Forecasting Australian Power Demand
Ali Eshragh, Benjamin Ganim, Terry Perkins +1
We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly time-scale, and show the crucial role of environmental factors i…
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
Kasun Bandara, Christoph Bergmeir, Hansika Hewamalage
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessa…
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statisti…
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Kasun Bandara, Peibei Shi, Christoph Bergmeir +3
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce fore…
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach
Kasun Bandara, Christoph Bergmeir, Slawek Smyl
With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with…