Kasun Bandara | DeepAI

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Featured Co-authors

Geoffrey I. Webb
44 publications
Christoph Bergmeir
34 publications
Ziyi Liu
24 publications
Rob J Hyndman
23 publications
Ali Eshragh
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Yanfei Kang
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Hansika Hewamalage
8 publications
Slawek Smyl
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Saman Halgamuge
7 publications
Rakshitha Wathsadini Godahewa
7 publications
Peibei Shi
2 publications

∙ 04/04/2023

Handling Concept Drift in Global Time Series Forecasting

Machine learning (ML) based time series forecasting models often require...

0 Ziyi Liu, et al. ∙

∙ 12/01/2022

A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study

We apply classical statistical methods in conjunction with the state-of-...

0 Saed Alizami, et al. ∙

∙ 06/22/2022

Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations

Distributed, small-scale solar photovoltaic (PV) systems are being insta...

0 Maneesha Perera, et al. ∙

∙ 07/28/2021

MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns

The decomposition of time series into components is an important task th...

0 Kasun Bandara, et al. ∙

∙ 12/30/2020

Ensembles of Localised Models for Time Series Forecasting

With large quantities of data typically available nowadays, forecasting ...

0 Rakshitha Wathsadini Godahewa, et al. ∙

∙ 12/23/2020

Global Models for Time Series Forecasting: A Simulation Study

In the current context of Big Data, the nature of many forecasting probl...

0 Hansika Hewamalage, et al. ∙

∙ 08/06/2020

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

Forecasting models that are trained across sets of many time series, kno...

0 Kasun Bandara, et al. ∙

∙ 04/25/2020

Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand

Emergency Medical Services (EMS) demand load has become a considerable b...

0 Kasun Bandara, et al. ∙

∙ 09/10/2019

LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns

Generating forecasts for time series with multiple seasonal cycles is an...

17 Kasun Bandara, et al. ∙

∙ 09/02/2019

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

Recurrent Neural Networks (RNN) have become competitive forecasting meth...

77 Hansika Hewamalage, et al. ∙

∙ 01/13/2019

Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology

Generating accurate and reliable sales forecasts is crucial in the E-com...

0 Kasun Bandara, et al. ∙

∙ 10/09/2017

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series

With the advent of Big Data, nowadays in many applications databases con...

0 Kasun Bandara, et al. ∙

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