COMPARING DEEP LEARNING MODELS AND TRADITIONAL MODEL IN ECONOMIC FORECASTING

Authors

  • Mo‘minov Baxodir Baltayevich Tashkent State University of Economics
  • Absalamova Diyora Bo‘riboyevna Tashkent State University of Economics

Keywords:

Deep Learning, economic forecasting, time series forecasting, RNNs, LSTMs

Abstract

In the ever-evolving landscape of economic analysis and prediction, the integration of cutting-edge technologies is reshaping the way we perceive and forecast economic trends. Among these transformative technologies, deep learning (DL) stands out as a powerful tool with the potential to revolutionize economic forecasting. In this article, we embark on an exploration of the fundamental concepts surrounding the integration of deep learning into economic forecasting, understanding how this synergy may redefine the accuracy and efficiency of predicting economic outcomes.

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Mo’minov B., Egamberdiyev E. MULTITIPLI MA’LUMOTLARGA INTELLEKTUAL ISHLOV BERISH //DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE. – 2023. – Т. 1. – №. 2. – С. 43-46.

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Published

2024-02-28

How to Cite

Mo‘minov , B., & Absalamova, D. (2024). COMPARING DEEP LEARNING MODELS AND TRADITIONAL MODEL IN ECONOMIC FORECASTING. DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2(1), 100–106. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v2i115