no code implementations • 4 Mar 2024 • Maneesha Perera, Julian De Hoog, Kasun Bandara, Damith Senanayake, Saman Halgamuge
In this work, we propose two deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region.
no code implementations • 7 Nov 2023 • Direnc Pekaslan, Jose Maria Alonso-Moral, Kasun Bandara, Christoph Bergmeir, Juan Bernabe-Moreno, Robert Eigenmann, Nils Einecke, Selvi Ergen, Rakshitha Godahewa, Hansika Hewamalage, Jesus Lago, Steffen Limmer, Sven Rebhan, Boris Rabinovich, Dilini Rajapasksha, Heda Song, Christian Wagner, Wenlong Wu, Luis Magdalena, Isaac Triguero
These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors.
1 code implementation • 4 Apr 2023 • Ziyi Liu, Rakshitha Godahewa, Kasun Bandara, Christoph Bergmeir
Handling concept drift in forecasting is essential for many ML methods in use nowadays, however, the prior work only proposes methods to handle concept drift in the classification domain.
no code implementations • 1 Dec 2022 • Saed Alizami, Kasun Bandara, Ali Eshragh, Foaad Iravani
While most mere machine learning methods are plagued by the lack of interpretability in practice, our novel hybrid approach will address this practical issue by generating explainable output.
1 code implementation • 22 Jun 2022 • Maneesha Perera, Julian De Hoog, Kasun Bandara, Saman Halgamuge
We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models.
1 code implementation • 30 Dec 2020 • Rakshitha Godahewa, Kasun Bandara, Geoffrey I. Webb, Slawek Smyl, Christoph Bergmeir
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 outperforming traditional univariate forecasting models that work on isolated series.
1 code implementation • 23 Dec 2020 • Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
Our experiments demonstrate that when trained as global forecasting models, techniques such as RNNs and LGBMs, which have complex non-linear modelling capabilities, are competitive methods in general under challenging forecasting scenarios such as series having short lengths, datasets with heterogeneous series and having minimal prior knowledge of the patterns of the series.
no code implementations • 6 Aug 2020 • Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir
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 real-world applications, outperforming many state-of-the-art univariate forecasting techniques.
no code implementations • 25 Apr 2020 • Kasun Bandara, Christoph Bergmeir, Sam Campbell, Deborah Scott, Dan Lubman
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, a warning sign of emerging problems.
3 code implementations • 10 Sep 2019 • Kasun Bandara, Christoph Bergmeir, Hansika Hewamalage
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays.
3 code implementations • 2 Sep 2019 • 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.
1 code implementation • 13 Jan 2019 • Kasun Bandara, Peibei Shi, Christoph Bergmeir, Hansika Hewamalage, Quoc Tran, Brian Seaman
Since the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model.
3 code implementations • 9 Oct 2017 • Kasun Bandara, Christoph Bergmeir, Slawek Smyl
In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 forecasting competition dataset.