Forecasts for international financial series with VMD algorithms

Guo, Wei; Liu, Qingfu; Luo, Zhidan; Tse, Yiuman

doi:10.1016/j.asieco.2022.101458

“…Overall results are more important to RMSE. It effectively penalizes significant errors and is expressed by (7):…”

Section: Resultsmentioning

confidence: 99%

“…Attention layer was added to LSTM to increase the weights of important data instances and improve LSTM's performance. VML [1] [7] introduced a hybrid method based on VMD, autoregressive integrated moving average (ARIMA), and Taylor expansion forecasting (TEF). They used VMD to decompose time series, ARIMA to forecast the linear component of each IMF, and TEF to predict the nonlinear component.…”

Section: Literature Reviewmentioning

confidence: 99%

“…So, it can simply isolate highly fluctuating data into lower frequency components [9]. Although many researchers used EMD for financial time series prediction, it has many inherent problems such as mode mixing, lack of mathematical foundations, sensitivity to noise and sampling, and choice of the interpolation method [7,15]. To address these shortcomings, variational mode decomposition (VMD) [15] was proposed by Dragomiretskiy and Zosso in 2014.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Nasiri¹,

Ebadzadeh²

2022

Preprint

5

0

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<p>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.</p>

show abstract

“…Compared with WT and EMD, VMD can achieve adaptive decomposition of signals by constructing and solving constrained variational problems, effectively avoiding problems such as mode mixing and boundary effects, and better performing complex signal decomposition. Guo et al ( 2022 ) applied the VMD algorithm to financial time series forecasting research and found that the forecasting performance of VMD-based ARIMA has a substantial improvement over ARIMA. Liu et al ( 2022 ) used WT, EMD, and VMD algorithms to forecast the carbon price separately.…”

Section: Literature Reviewmentioning

confidence: 99%

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

Xu

¹

,

Wang

²

,

Zhang

³

et al. 2022

View full text Add to dashboard Cite

“…Obviously, a long-term prediction (i.e., multi-step ahead) is more beneficial for stock market investors to arrive at the best decisions and plans for the future. Nevertheless, most existing stock price prediction researches [6][7][8][9] only address one-step ahead forecasting. Therefore, there is a need to investigate multi-step ahead prediction of stock prices [2].…”

Section: Introductionmentioning

confidence: 99%

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

Nasiri¹,

Ebadzadeh²

2022

Preprint

1

0

View full text Add to dashboard Cite

<p>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.</p>

show abstract

Forecasts for international financial series with VMD algorithms

Cited by 25 publications

References 43 publications

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

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

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

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

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