Cuban consumer price index forecasting through transformer with attention

• Autorzy: Rosado Reynaldo , Toledano-López Orlando G. , González Hector R. , Abreu Aldis J. , Hernandez Yanio

Identyfikatory

DOI

10.14313/JAMRIS/2‐2023/11

• Języki publikacji: EN

Abstrakty

EN

Recently, time series forecasting modelling in the Con‐ sumer Price Index (CPI) has attracted the attention of the scientific community. Several research projects have tackled the problem of CPI prediction for their countries using statistical learning, machine learning and deep neural networks. The most popular approach to CPI in several countries is the Autoregressive Integrated Mov‐ ing Average (ARIMA) due to the nature of the data. This paper addresses the Cuban CPI forecasting problem using Transformer with attention model over univariate dataset. The fine tuning of the lag parameter shows that Cuban CPI has better performance with small lag and that the best result was in 𝑝 = 1. Finally, the comparative results between ARIMA and our proposal show that the Transformer with attention has a very high performance despite having a small data set.

Słowa kluczowe

EN

consumer price index; time series forecasting; transformer with attention; ARIMA; LSTM

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2023
• Tom: Vol. 17, No. 2
• Strony: 12--17
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Rosado Reynaldo

• Universidad de las Ciencias Informáticas, Cuba

autor

Toledano-López Orlando G.

• Universidad de las Ciencias Informáticas, Cuba

autor

González Hector R.

• Universidad de las Ciencias Informáticas, Cuba

• https://orcid.org/0000-0002-7601-4201

autor

Abreu Aldis J.

• Universidad de las Ciencias Informáticas, Cuba

autor

Hernandez Yanio

• Universidad de las Ciencias Informáticas, Cuba

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).