International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/2656 2020 2020 Finance and Business Sector, Institute of Public Administration, Riyadh 11141, P. O. Box 205, Saudi Arabia Abdelgalal Abdelgalal Department of Economics and Finance, Business College, Taif University, P. O Box 11099, Taif 21944, Saudi Arabia Khalil A. Alruwaitee Department of Basic Science, College of Preparatory year, Najran University, Najran, 61441, Saudi Arabia Sulima M. Awad Yousif Department of Psychology, College of Education, King Khalid University, Abha, Saudi Arabia Ashraf A. Awad Alotaibi Applied College, Khamis Mushait, King Khalid University, Abha, Saudi Arabia Abdelgalal O. I. Abaker Department of Mathematics and Statistics, College of Science, P.O. Box 11099, Taif University, Taif 21944, Saudi Arabia Azhari A. Elhag Applied statistics has been instrumental in predicting behaviours and future market trends. In the field of financial time series analysis, the incorporation of deep learning (DL) methods and applied statistics has made a significant contribution to the prediction model. Practitioners and researchers can extract complex features and dependencies from past financial data by leveraging neural network structures like long short-term memory (LSTM) and recurrent neural networks (RNNs). These DL approaches advance the development of predictive models prone to forecasting different financial metrics, such as asset returns, stock prices, and market volatility, with outstanding accuracy. With the combination of statistical approaches with DL techniques, researchers can leverage the power of both worlds to make more informed investment decisions and improve forecasting capabilities in volatile and dynamic financial markets. This study develops a new Applied Statistics with Single Valued Neutrosophic Fuzzy Soft Expert Sets (AS-SVNFSES) technique for Financial Time Series Forecasting. The presented AS-SVNFSES technique aims to forecast the input financial time series data. The AS-SVNFSES technique primarily applies data preprocessing using a Z-score normalization approach. For the forecasting of financial data, the AS-SVNFSES technique makes use of the SVNFSES technique. Finally, the parameter tuning of the SVNFSES technique is performed using the chimp optimization algorithm's (ChOA) design. A series of experimentations have illustrated the amended performance of the AS-SVNFSES model. The experimental value inferred that the AS-SVNFSES technique gains improved performance over other models. 2024 2024 159 170 10.54216/IJNS.240115 https://www.americaspg.com/articleinfo/21/show/2656