What question did this study set out to answer?

The study aims to introduce a novel paradigm for big data classification to improve stock price forecasting accuracy.

January 18, 2024

Ensemble Model for Stock Price Forecasting: MapReduce Framework for Big Data Handling: An Optimal Trained Hybrid Model for Classification

Key Points

The study aims to introduce a novel paradigm for big data classification to improve stock price forecasting accuracy.
Introduced a preprocessing, feature extraction and classification framework using the MapReduce technology.
Applied a Bi-LSTM and deep maxout hybrid model for classification during data reduction.
Employed the Hybrid Butterfly Positioned Coot Optimization algorithm for training and optimizing accuracy.
Achieved classification accuracy of 97.45%, surpassing methods like NN (85.13%) and CNN (83.78%).
Demonstrated significant improvements over existing models: RNN (78.37%), Bi-LSTM (82.43%), and SVM (87.83%).

Abstract

A number of authors have focused on this study to examine how huge data are perceived. A novel big data classification paradigm is introduced by the work’s preprocessing, feature extraction and classification techniques. Data normalization is carried out at the preprocessing stage. The MapReduce framework is then utilized to manage the massive data. Statistical features (mean, median, min/max and SD), higher-order statistical features (skewness, kurtosis and enhanced entropy), and correlation-based features are all extracted prior to classification. The Bi-LSTM and deep maxout hybrid classification model classifies the data during the reduction stage. To assure classification accuracy, training will also be deployed by the new Hybrid Butterfly Positioned Coot Optimization (HBPCO) algorithm. The proposed method’s accuracy of 97.45% beats the methods of NN (85.13%), CNN (83.78%), RNN (78.37%), Bi-LSTM (82.43%) and SVM (87.83%).

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