Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural
Network and Variational Mode Decomposition
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.