论文标题
使用经常性模糊神经网络和变分模式分解的多步股价预测
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition
论文作者
论文摘要
财务时间序列预测是一个越来越多的研究主题，引起了学者的浓厚兴趣，并且已经开发出了几种方法。其中，基于分解的方法已取得了有希望的结果。大多数基于分解的方法近似于单个函数，这不足以获得准确的结果。此外，大多数现有研究都集中在一步预测上，以防止股市投资者对未来做出最佳决策。这项研究提出了两种基于概述的问题的多步股价预测的新方法。 DCT-MFRFNN是一种基于离散余弦变换（DCT）和多功能复发性模糊神经网络（MFRFNN）的方法，它使用DCT减少时间序列中的波动，并简化其结构和MFRFNN来预测股票价格。 VMD-MFRFNN是一种基于变分模式分解（VMD）和MFRFNN的方法，将它们的优势汇总在一起。 VMD-MFRFNN由两个阶段组成。输入信号在分解阶段使用VMD分解为多个IMF。在预测和重建阶段中，每个IMF都予以预测的单独MFRFNN，并将预测的信号求和以重建输出。三个财务时间序列，包括Hang Seng Index（HSI），上海证券交易所（SSE）和Standard＆Poor的500指数（SPX），用于评估拟议方法。实验结果表明，VMD-MFRFNN超过其他最先进的方法。平均而言，VMD-MFRFNN显示了RMSE的35.93％，24.88％和34.59％分别从HSI，SSE和SPX的第二好的模型中降低。此外，在所有实验中，DCT-MFRFNN均优于MFRFNN。
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.
