# State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge

arXiv: https://arxiv.org/abs/2112.03595 · HTML: https://mlsanity.com/paper/2112.03595/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Built on by (most cited first, 8 of 8)

- [Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices](https://mlsanity.com/paper/2203.10716/) (2022)
- [Predict+Optimize Problem in Renewable Energy Scheduling](https://mlsanity.com/paper/2212.10723/) (2022)
- [Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs](https://mlsanity.com/paper/2202.12595/) (2022)
- [How to predict and optimise with asymmetric error metrics](https://mlsanity.com/paper/2211.13586/) (2022)
- [Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure](https://mlsanity.com/paper/2308.03472/) (2023)
- [The intersection of machine learning with forecasting and optimisation: theory and applications](https://mlsanity.com/paper/2211.13583/) (2022)
- [Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework](https://mlsanity.com/paper/2312.03413/) (2023)
- [Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment](https://mlsanity.com/paper/2404.14635/) (2024)

## Abstract (arXiv)

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 ensemble of LightGBM models and the prescriptive analysis employs mathematical optimisation to efficiently prescribe solutions that minimise the average cost over multiple scenarios. Our solutions ranked 1st in the optimisation and 2nd in the prediction challenge of the competition.