@article{Önal Tuğrul2024, 
author = {Nisa Özge Önal Tuğrul and Kamil Karaçuha and Esra Ergün and Vasil Tabatadze and Ertuğrul Karaçuha},
title = {A novel modeling and prediction approach using Caputo derivative: An economical review via multi-deep assessment methodology},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {9},
pages = {23512-23543},
keywords = {Caputo fractional derivative, deep assessment methodology, mathematical modeling, time series prediction},
url = {https://www.sciopen.com/article/10.3934/math.20241143},
doi = {10.3934/math.20241143},
abstract = {In this study, we proposed a novel modeling and prediction method employing both fractional calculus and the multi-deep assessment methodology (M-DAM), utilizing multifactor analysis across the entire dataset from 2000 to 2019 for comprehensive data modeling and prediction. We evaluated and reported the performance of M-DAM by modeling various economic factors such as current account balance (% of gross domestic product (GDP)), exports of goods and services (% of GDP), GDP growth (annual %), gross domestic savings (% of GDP), gross fixed capital formation (% of GDP), imports of goods and services (% of GDP), inflation (consumer prices, annual %), overnight interbank rate, and unemployment (total). The dataset used in this study covered the years between 2000 and 2019. The Group of Eight (G-8) countries and Turkey were chosen as the experimental domain. Furthermore, to understand the validity of M-DAM, we compared the modeling performance with multiple linear regression (MLR) and the one-step prediction performance with a recurrent neural network, long short-term memory (LSTM), and MLR. The results showed that in 75.04% of the predictions, M-DAM predicted the factors with less than 10% error. For the order of predictability considering the years 2018 and 2019, Germany was the most predictable country; the second group consisted of Canada, France, the UK, and the USA; the third group included Italy and Japan; and the fourth group comprised Russia. The least predictable country was found to be Turkey. Comparison with LSTM and MLR showed that the three methods behave complementarily.}
}