Forecasting of Key Performance Indicators Based on Transformer Model
Year: 2024
Type: conference-paper
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.
Institution: Marche Polytechnic University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4396573338