Forecasting of Key Performance Indicators Based on Transformer Model

Claudia Diamantini, Tarique Khan, Alex Mircoli, Domenico Potena

2024

Abstract

Key performance indicators (KPIs) express the company’s strategy and vision in terms of goals and enable alignment with stakeholder expectations. In business intelligence, forecasting KPIs is pivotal for strategic decision-making. For this reason, in this work we focus on forecasting KPIs. We built a transformer model architecture that outperforms conventional models like Multi-Layer Perceptrons (MLP), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) in KPI forecasting over the Rossmann Store, supermarket 1, and 2 datasets. Our results highlight the revolutionary potential of using cutting-edge deep learning models such as the Transformer.

Paper Citation

in Harvard Style

Diamantini C., Khan T., Mircoli A. and Potena D. (2024). Forecasting of Key Performance Indicators Based on Transformer Model. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 280-287. DOI: 10.5220/0012726500003690

in Bibtex Style

@conference{iceis24,
author={Claudia Diamantini and Tarique Khan and Alex Mircoli and Domenico Potena},
title={Forecasting of Key Performance Indicators Based on Transformer Model},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2024},
pages={280-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012726500003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Forecasting of Key Performance Indicators Based on Transformer Model
SN - 978-989-758-692-7
AU - Diamantini C.
AU - Khan T.
AU - Mircoli A.
AU - Potena D.
PY - 2024
SP - 280
EP - 287
DO - 10.5220/0012726500003690
PB - SciTePress