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04/04/2023
Handling Concept Drift in Global Time Series Forecasting
Machine learning (ML) based time series forecasting models often require...
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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-...
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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...
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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...
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12/30/2020
Ensembles of Localised Models for Time Series Forecasting
With large quantities of data typically available nowadays, forecasting ...
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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...
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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...
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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...
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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...
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09/02/2019
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Recurrent Neural Networks (RNN) have become competitive forecasting meth...
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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...
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10/09/2017
