14 citations · 46 across the 14 of their papers we have counts for
9 papers · 1 filter
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
Lucas English, Mahdi Abolghasemi
Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncerta…
How to predict and optimise with asymmetric error metrics
Mahdi Abolghasemi, Richard Bean
In this paper, we examine the concept of the predict and optimise problem with specific reference to the third Technical Challenge of the IEEE Computational Intelligence Society. I…
The intersection of machine learning with forecasting and optimisation: theory and applications
Mahdi Abolghasemi
Forecasting and optimisation are two major fields of operations research that are widely used in practice. These methods have contributed to each other growth in several ways. Howe…
Forecasting sales with Bayesian networks: a case study of a supermarket product in the presence of promotions
Muhammad Hamza, Mahdi Abolghasemi, Abraham Oshni Alvandi
Sales forecasting is the prerequisite for a lot of managerial decisions such as production planning, material resource planning and budgeting in the supply chain. Promotions are on…
State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge
Mahdi Abolghasemi, Rasul Esmaeilbeigi
In this paper, we describe our proposed methodology to approach the predict+optimise challenge introduced in the IEEE CIS 3rd Technical Challenge. The predictive model employs an e…
How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function
Mahdi Abolghasemi, Babak Abbasi, Toktam Babaei +1
Using machine learning in solving constraint optimization and combinatorial problems is becoming an active research area in both computer science and operations research communitie…
Model selection in reconciling hierarchical time series
Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis +1
Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart fro…
Hierarchical forecast reconciliation with machine learning
Evangelos Spiliotis, Mahdi Abolghasemi, Rob J Hyndman +2
Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical…
Machine learning applications in time series hierarchical forecasting
Mahdi Abolghasemi, Rob J Hyndman, Garth Tarr +1
Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a de…