Forecasting of Key Performance Indicators Based on Transformer Model

Claudia Diamantini; Tarique Khan; Alex Mircoli; Domenico Potena

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Paper

Forecasting of Key Performance Indicators Based on Transformer Model

Topics: Dependable Systems for Critical Applications; Domain Specific and Multi-aspect IS Engineering; Knowledge Management; Modeling of Distributed Systems; Requirements Analysis And Management

In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS, 280-287, 2024 , Angers, France

Authors: Claudia Diamantini ¹ ; Tarique Khan ¹ ; Alex Mircoli ² and Domenico Potena ¹

Affiliations: ¹ Department of Information Engineering, Università Politecnica delle Marche, Italy ; ² Department of Economic and Social Sciences, Università Politecnica delle Marche, Italy

Keyword(s): Deep Learning, Neural Networks, Transformer, Organization Process Management, KPIs, Performance Indicators.

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.

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Paper citation in several formats:

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; ISSN 2184-4992, SciTePress, pages 280-287. DOI: 10.5220/0012726500003690

@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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Diamantini, C.
AU - Khan, T.
AU - Mircoli, A.
AU - Potena, D.
PY - 2024
SP - 280
EP - 287
DO - 10.5220/0012726500003690
PB - SciTePress