Log-Quad divergence for Non-negative Matrix Factorization in multi-model prediction

Ryszard Szupiluk; Paweł Rubach

doi:https://doi.org/10.59139/ws.2024.05.2

Ryszard Szupiluk Szkoła Główna Handlowa w Warszawie, Instytut Informatyki i Gospodarki Cyfrowej, Polska / Warsaw School of Economics, Institute of Information Systems and Digital Economy, Poland. ORCID: https://orcid.org/0000-0002-4416-7131 , Paweł Rubach Szkoła Główna Handlowa w Warszawie, Instytut Informatyki i Gospodarki Cyfrowej, Polska / Warsaw School of Economics, Institute of Information Systems and Digital Economy, Poland ORCID: https://orcid.org/0000-0001-5487-609X Wiadomości Statystyczne. The Polish Statistician, vol. 69, 2024, 5, s. 14-24 Opublikowano online: 31 maja 2024 DOI https://doi.org/10.59139/ws.2024.05.2 Sposób cytowania: Szupiluk, R., Rubach, P. (2024). Log-Quad divergence for Non-negative Matrix Factorization in multi-model prediction. Wiadomości Statystyczne. The Polish Statistician, 69(5), 14–24. https://doi.org/10.59139/ws.2024.05.2.

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ARTYKUŁ

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STRESZCZENIE

The aim of this paper is to present a new Non-negative Matrix Factorization (NMF) algorithm based on Log-Quad divergence, and to demonstrate its application to the separation of latent destructive components contained in prediction results in a multi-model approach. We provide an example of its application to a real economic problem, i.e. forecasting electricity consumption on the basis of information about hourly use of electricity in Poland in the period of 1988–1997. We evaluated and compared this method with other blind signal (source) separation techniques, such as Independent Component Analysis (ICA) and Algorithm for Multiple Unknown Signals Extraction (AMUSE). The results show that the NMF algorithm based on Log-Quad divergence has an interesting ability to improve predictions for small volumes of data.

SŁOWA KLUCZOWE

Non-negative Matrix Factorization, NMF, latent components identification, blind source separation, blind signal separation, prediction, ICA, AMUSE

JEL

C02, C50

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