Multi-step-ahead stock price prediction using recurrent fuzzy neural network and variational mode decomposition

Hamid Nasiri · Mohammad Mehdi Ebadzadeh

78 citations0/0 files storedApplied Soft Computing

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Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

Abstract

Financial time series prediction, a growing research topic, has attracted considerable interest from scholars, and several approaches have been developed. Among them, decomposition-based methods have achieved promising results. Most decomposition-based methods approximate a single function, which is insufficient for obtaining accurate results. Moreover, most existing researches have concentrated on one-step-ahead forecasting that prevents stock market investors from arriving at the best decisions for the future. This study proposes two novel methods for multi-step-ahead stock price prediction based on the issues outlined. DCT-MFRFNN, a method based on discrete cosine transform (DCT) and multi-functional recurrent fuzzy neural network (MFRFNN), uses DCT to reduce fluctuations in the time series and simplify its structure and MFRFNN to predict the stock price. VMD-MFRFNN, an approach based on variational mode decomposition (VMD) and MFRFNN, brings together their advantages. VMD-MFRFNN consists of two phases. The input signal is decomposed to several IMFs using VMD in the decomposition phase. In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output. Three financial time series, including Hang Seng Index (HSI), Shanghai Stock Exchange (SSE), and Standard & Poor's 500 Index (SPX), are used for the evaluation of the proposed methods. Experimental results indicate that VMD-MFRFNN surpasses other state-of-the-art methods. VMD-MFRFNN, on average, shows 35.93%, 24.88%, and 34.59% decreases in RMSE from the second-best model for HSI, SSE, and SPX, respectively. Also, DCT-MFRFNN outperforms MFRFNN in all experiments.

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References

2017Forecasting at Scale 2020Stock price prediction using deep learning and frequency decomposition 2019Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network 2016A variational mode decompoisition approach for analysis and forecasting of economic and financial time series 2020A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network 2021Forecasting stock index price using the CEEMDAN-LSTM model 2022Novel optimization approach for stock price forecasting using multi-layered sequential LSTM 2020A new crude oil price forecasting model based on variational mode decomposition

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