Swarnalata Rath

Work place: Department of Computer Science and Engineering, Siksha ‗O‘ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India - 751030

E-mail: Swarnalata.rath@gmail.com

Website: https://orcid.org//0000-0003-0318-1065

Research Interests:

Biography

Swarnalata Rath is currently working as an Assistant Professor in the Department of Computer Science & Engineering, NMIET, Bhubaneswar, Odisha. She is currently pursuing Ph.D, in Computer Science &Engineering at Siksha "O" Anusandhan(Deemed to be University), Odisha. She has published 2 International Journal papers, 2 Conference papers and 1 patents. She has attended various Seminars, Conferences and Workshops at National level and International level.

Author Articles
Next-Gen Market Predictor: Transformed Moving Average Fast-RNN Hybrid with Advanced CNNS

By Swarnalata Rath Nilima R. Das Binod Kumar Pattanayak

DOI: https://doi.org/10.5815/ijigsp.2025.03.06, Pub. Date: 8 Jun. 2025

Stock price prediction anticipates future stock prices using historical data and computational models to assist and guide investing decisions. In financial forecasting, accuracy and efficacy in stock price prediction are essential for making better choices. This research describes a hybrid deep learning strategy for improving the extraction and interpretation of the crucial details from stock price time series data. Traditional approaches confront challenges such as computational complexity and nonlinear stock prices. The suggested method pre-processes stock data with Moving Average Z-Transformation, which emphasises long-term trends and reduces fluctuations in the short term. It combines a Transformed Moving Average Fast-RNN Hybrid with Advanced CNNs to create an efficient computational framework. The Enhanced Deep-CNN layer comprises convolutional layers, batch normalisation, leaky ReLU activations, dropout, max pooling and a dense layer. The performance of the model is quantified using metrics including Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2). It shows superior prediction accuracy with MAEs of 0.28, 0.15, 0.34, 0.17, and 0.13 for Kotak, ICICI, Axis, and SBI, respectively, outperforming previous models. These measurements provide detailed information about the model's predictive skills, proving its ability to improve stock price forecast accuracy significantly.

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