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Found 12 papers, 1 papers with code
Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment
Prediction and optimisation are two widely used techniques that have found many applications in solving real-world problems.
Humans vs Large Language Models: Judgmental Forecasting in an Era of Advanced AI
This study investigates the forecasting accuracy of human experts versus Large Language Models (LLMs) in the retail sector, particularly during standard and promotional sales periods.
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework
In this paper we develop neural network models to approximate Knapsack Problem solutions using the Lagrangian Dual Framework while improving constraint satisfaction.
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
Renewable energy generation is of utmost importance for global decarbonization.
Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
How to predict and optimise with asymmetric error metrics
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.
The intersection of machine learning with forecasting and optimisation: theory and applications
We advocate the integration of these two fields and explore several problems that require both forecasting and optimisation to deal with the uncertainties.
State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge
In this paper, we describe our proposed methodology to approach the predict+optimise challenge introduced in the IEEE CIS 3rd Technical Challenge.
How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function
It also investigates the importance of loss function and error criterion in machine learning models where they are used for predicting solutions of optimization problems.
Model selection in reconciling hierarchical time series
However, when dealing with hierarchical time series, apart from selecting the most appropriate forecasting model, forecasters have also to select a suitable method for reconciling the base forecasts produced for each series to make sure they are coherent.
Hierarchical forecast reconciliation with machine learning
First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches.
Machine learning applications in time series hierarchical forecasting
We perform an in-depth analysis of 61 groups of time series with different volatilities and show that ML models are competitive and outperform some well-established models in the literature.
