Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Lu WangThis email address is being protected from spambots. You need JavaScript enabled to view it.

Shunde Polytechnic, Foshan, Guangdong, China, 528333


 

 

Received: May 18, 2023
Accepted: August 27, 2023
Publication Date: November 16, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202408_27(8).0005  


Organizations are becoming more productive due to the digital economy because they can presently employ technologies that automate various tasks and procedures. The digital economy, which is driven by the network infrastructure, is an advanced field that is constantly evolving. As globalization continues to advance, so do customers’ perceptions of financial forecasting and analysis, even though the conventional economic evaluation and prediction model has relatively poor forecasting ability. Hence, the study offered improved time series strategies (ITSS) for productive advancements in the digital economy (DE)by adapting the time series framework. The research used the Technology-Organization-Environment (TOE) model on a systematic similar evaluation to investigate the digital economy in all 31 regions in China.The effectiveness of regional digital economy growth is presented in-depth in this study, with an emphasis on the interaction between technology, organization, and environment.Understanding and resolving socioeconomic, cultural, and regulatory variables in the environment dimension helps overcome difficulties and build trust. These TOE aspects could stimulate digital economy development, promoting sustainable growth and equitable prosperity in the age of technology. The study has also underlined the importance and necessity of developing the digital economy in an approach that is both environmentally sustainable and economically efficient. The findings reveal that the proposed approach has been well efficient in the application of time series models in forecasting the efficiency of digital economy development. Our method can provide 96% accuracy while being 92% prediction rate, 97% prediction time, 91% error rate, and 95 % electrode placement efficiency.


Keywords: digital economy, financial forecasting, improved time series strategies (ITSS), Technology–Organization–Environment (TOE).


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