Article: Enhancing stock market prediction through image encoding, pattern recognition, and ensemble learning with custom error correction techniques Journal: International Journal of Computational Vision and Robotics (IJCVR) 2024 Vol.14 No.6 pp.654 - 676 Abstract: Financial forecasting is a crucial task in the financial sector and is currently being addressed using various technical pricing patterns. However, the conventional techniques suffer from limitations such as time-consuming computations and lower accuracy due to the stochastic dependency between historical and future values. This research aims to bridge the gap in financial forecasting by proposing a hybrid model that combines time series analysis using LSTM with image processing techniques such as Gramain Angular Field, line plot methodology, and error correction techniques. The proposed approach leverages the strengths of both techniques to provide a reliable forecasting solution that can capture the stochastic dependency between past and future values. The study aims to contribute to the field of machine learning by providing a novel approach to financial forecasting and expanding the research on intelligent processing methods. For Apple, when compared the LSTM model result with final model there is ~48% decrease in test RMSE and ~57% decrease in test MAE. For Amazon, when compared the LSTM model result with final model there is ~14% decrease in test RMSE and ~10% decrease in test MAE. Moreover, the proposed model outclasses the state-of-art model and addresses the overfitting in them. Inderscience Publishers - linking academia, business and industry through research

Title: Enhancing stock market prediction through image encoding, pattern recognition, and ensemble learning with custom error correction techniques

Authors: Ravi Prakash Varshney; Dilip Kumar Sharma

Addresses: Department of Computer Engineering and Applications, GLA University, Mathura, India ' Department of Computer Engineering and Applications, GLA University, Mathura, India

Abstract: Financial forecasting is a crucial task in the financial sector and is currently being addressed using various technical pricing patterns. However, the conventional techniques suffer from limitations such as time-consuming computations and lower accuracy due to the stochastic dependency between historical and future values. This research aims to bridge the gap in financial forecasting by proposing a hybrid model that combines time series analysis using LSTM with image processing techniques such as Gramain Angular Field, line plot methodology, and error correction techniques. The proposed approach leverages the strengths of both techniques to provide a reliable forecasting solution that can capture the stochastic dependency between past and future values. The study aims to contribute to the field of machine learning by providing a novel approach to financial forecasting and expanding the research on intelligent processing methods. For Apple, when compared the LSTM model result with final model there is ~48% decrease in test RMSE and ~57% decrease in test MAE. For Amazon, when compared the LSTM model result with final model there is ~14% decrease in test RMSE and ~10% decrease in test MAE. Moreover, the proposed model outclasses the state-of-art model and addresses the overfitting in them.

Keywords: time series forecasting; Gramain Angular field; GAF; computer vision; pattern recognition; image encoding; error correction.

DOI: 10.1504/IJCVR.2024.141812

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.6, pp.654 - 676

Received: 31 Dec 2022
Accepted: 22 Mar 2023
Published online: 02 Oct 2024 *

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Title: Enhancing stock market prediction through image encoding, pattern recognition, and ensemble learning with custom error correction techniques

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