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Decomposition techniques and long short term memory model with black widow optimization for stock price prediction

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Abstract

Accurate stock price prediction is a trivial task because of the highly non-linear nature of the time series of stock prices. Numerous time series decomposition methods are applied to manage the extensively non-linear nature of the time series data, which decomposes the non-linear time series into linear components. This paper decomposes non-linear and non-stationary time series data into linear components by the joint decomposition method, namely empirical mode decomposition and local mean decomposition. By using the joint decomposition technique, we can better address potential issues such as mode mixing or the loss of information that may arise when using individual EMD or LMD. This leads to a more accurate and reliable analysis of the signal, improving the quality of the results obtained. The empirical mode decomposition is applied to the initial stock price data to generate distinct intrinsic mode functions. The local mean decomposition is applied to each intrinsic mode function to find a series of the product of functions. The forecasting model, long short-term memory is used for each product of function along with weight optimization techniques to achieve precise predictions for the time series. For optimizing the weight of the LSTM model, the black widow optimization technique is used. The forecast series of each product of function is aggregated to obtain the final series. The efficiency of the proposed hybrid technique is evaluated through the mean absolute error, root mean square error, and mean absolute percentage error. Our results demonstrate the proposed model yields the best performance and outperforms the existing forecasting models with respect to accuracy and efficiency. The proposed model EMD-LMD-LSTM-BWO enhances the performance of the LSTM model. In comparison to the LSTM, LSTM-BWO, EMD-LSTM, EMD-LSTM-BWO, and EMD-LMD-LSTM models, the proposed model has a percentage improvement of 89.03, 88.06, 73.81, 73.01, and 15.78, respectively, when mean absolute error values are taken into account.

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  1. Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, 462003, Bhopal, Madhya Pradesh, India

    Varsha Kushwah & Pragati Agrawal

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Kushwah, V., Agrawal, P. Decomposition techniques and long short term memory model with black widow optimization for stock price prediction. Multimed Tools Appl 83, 37453–37481 (2024). https://doi.org/10.1007/s11042-023-17062-6

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