2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 18 , Issue 2 , PP: 100-109, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Denis Shakhov 1 * , Inomjon Yusubov 2 , Sanat Yakubov 3 , Aleksey Ilyin 4 , Emil Hajiyev 5 , Tatyana Khorolskaya 6
Doi: https://doi.org/10.54216/FPA.180208
Prediction of time series is a vital issue related to an extensive array of financial, and social applications, and engineering. The main challenge arises from the intricacy due to the temporal assets of time series and the unavoidable weakening function of analytical systems. Therefore, it is usually problematic to precisely forecast values, particularly in a multi-step ahead situation. Multi-step financial stock price forecast over a lasting perspective is vital for predicting its instability, letting economic organizations charge and evade derivatives, and banks to measure the hazard. Recently, Deep learning systems have been capable to perceive and analyze intricate patterns and connections in the data automatically and haste up the trading procedure. This manuscript designs and develops a Multi-Step Financial Stock Index Forecasting Model Using a Convolutional Neural Network with Gated Recurrent Unit (MFSIFM-CNNGRU) model. The proposed MFSIFM-CNNGRU model relies on enhancing the predicting model for the financial stock index. To accomplish that, the data normalization stage is initially performed by employing z-score normalization to convert input data into a suitable format. Next, the proposed MFSIFM-CNNGRU model designs a hybrid of convolutional neural network and gated recurrent unit (CNN-GRU) technique for the prediction model. Eventually, the hyperparameter selection of the CNN-GRU model can be implemented by the design of the improved whale optimization algorithm (IWOA). The efficiency of the MFSIFM-CNNGRU method has been validated by comprehensive studies using the benchmark dataset. The numerical result shows that the MFSIFM-CNNGRU method has better performance and scalability under various measures over the recent techniques
Multi-Step Financial Stock Index , Convolutional Neural Network , Gated Recurrent Unit , Z-score Normalization , Improved Whale Optimization Algorithm
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