Abstract
The goal of stock market prediction using various techniques of machine learning and deep learning is to create more accurate models. Stock price prediction from multidomain datasets requires design of data collection, pre-processing, aggregation, pre-filtering, feature representation and variance-based selection, prediction model design, and post processing processes. Existing models that perform these tasks are highly context-sensitive and cannot be scaled for multiple stock markets. Most of these models do not incorporate continuous learning mechanisms, which limits their scalability. To overcome these issues, this work proposes, design of a novel Grey Wolf Optimization (GWO) based model for continuous optimization of multidomain stock prediction performance. It aggregates datasets from multiple sources like stock prices, global news, local news, social media information, and commodity prices. The model is developed using Convolutional Neural Networks (CNNs), which assisted in providing high accuracy for multiple stock types. The CNN based model’s performance is continuously tuned via use of a GWO based optimization layer which assists in incremental learning to optimize stock prediction efficiency for multiple use cases. The proposed GWO model uses a fitness function that incorporates prediction accuracy, computational delay, and precision values in order to identify optimal CNN configuration for better performance. Performance of the model was tested on Indian, American and Chinese stock markets and compared with various state-of-the-art models. Based on this comparison, it is observed that the proposed model has improved prediction accuracy by 7.6% and precision by 8.5%, while reducing computational delay by 5.9% across multiple evaluations.
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Sable, R., Goel, S., Chatterjee, P. (2023). GCOMSP: Design of a GWO Based Model for Continuous Optimization of Multidomain Stock Prediction Performance. In: Garg, D., Narayana, V.A., Suganthan, P.N., Anguera, J., Koppula, V.K., Gupta, S.K. (eds) Advanced Computing. IACC 2022. Communications in Computer and Information Science, vol 1782. Springer, Cham. https://doi.org/10.1007/978-3-031-35644-5_1
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