scholarly journals Financial Market Prediction and Simulation Based on the FEPA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dehui Zhou
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Financial Market ◽  
Financial Time Series ◽  
Principal Component ◽  
Stock Index ◽  
Financial Time ◽  
Mode Decomposition ◽  
Bpnn Model

Since the birth of the financial market, the industry and academia want to find a method to accurately predict the future trend of the financial market. The ultimate goal of this paper is to build a mathematical model that can effectively predict the short-term trend of the financial time series. This paper presents a new combined forecasting model: its name is Financial Time Series-Empirical Mode Decomposition-Principal Component Analysis-Artificial Neural Network (FEPA) model. This model is mainly composed of three components, which are based on financial time series special empirical mode decomposition (FTA-EMD), principal component analysis (PCA), and artificial neural network. This model is mainly used to model and predict the complex financial time series. At the same time, the model also predicts the stock market index and exchange rate and studies the hot fields of the financial market. The results show that the empirical mode decomposition back propagation neural network (EMD-BPNN) model has better prediction effect than the autoregressive comprehensive moving average model (ARIMA), which is mainly reflected in the accuracy of prediction. This shows that the prediction method of decomposing and recombining nonlinear and nonstationary financial time series can effectively improve the prediction accuracy. When predicting the closing price of Australian stock index, the hit rate (DS) of the FEPA model decomposition method is 72.22%, 10.86% higher than the EMD-BPNN model and 3.23% higher than the EMD-LPP-BPNN model. When the FEPA model predicts the Australian stock index, the hit rate is improved to a certain extent, and the effect is better than other models.

scholarly journals Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Abobaker M. Jaber ◽  
Mohd Tahir Ismail ◽  
Alsaidi M. Altaher
Keyword(s):  
Time Series ◽  
Quantile Regression ◽  
Empirical Mode Decomposition ◽  
Financial Time Series ◽  
Stock Index ◽  
Financial Time ◽  
Local Linear ◽  
Mode Decomposition ◽  
Closing Price ◽  
Index Time Series

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

APPLICATION OF EMPIRICAL MODE DECOMPOSITION COMBINED WITH k-NEAREST NEIGHBORS APPROACH IN FINANCIAL TIME SERIES FORECASTING

2012 ◽  
Vol 11 (02) ◽  
pp. 1250018 ◽  
Author(s):  
AIJING LIN ◽  
PENGJIAN SHANG ◽  
GUOCHEN FENG ◽  
BO ZHONG
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Financial Time Series ◽  
Nearest Neighbors ◽  
Stock Index ◽  
New Technique ◽  
K Nearest Neighbors ◽  
Mode Decomposition ◽  
Recent Trends ◽  
A New Technique

The purpose of this paper is to forecast the daily closing prices of stock markets based on the past sequences. In this paper, keeping in mind the recent trends and the limitations of previous researches, we proposed a new technique, called empirical mode decomposition combined with k-nearest neighbors (EMD–KNN) method, in forecasting the stock index. EMD–KNN takes the advantages of the KNN and EMD. To demonstrate that our EMD–KNN method is robust, we used the new technique to forecast four stock index time series at a specific time. Detailed experiments are implemented for both of the proposed forecasting models, in which EMD–KNN, KNN method and ARIMA are compared. The results demonstrate that the proposed EMD–KNN model is more successful than KNN method and ARIMA in predicting the stock closing prices.

scholarly journals Deep Learning Algorithm-Based Financial Prediction Models

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Helin Jia
Keyword(s):  
Time Series ◽  
Foreign Exchange ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Financial Time Series ◽  
Principal Component ◽  
Stock Index ◽  
Forecasting Model ◽  
Financial Time ◽  
Closing Price

In this paper, a new FEPA portfolio forecasting model is based on the EMD decomposition method. The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, multiscale complex financial time series to predict stock market indices and foreign exchange rates and empirically investigate this hot area in financial market research. The combined forecasting model proposed in this paper is based on the idea of decomposition-reconstruction synthesis, which effectively improves the model’s prediction of internal financial time series. In this paper, we select the CSI 300 Index and foreign exchange rate as the empirical market and data and establish seven forecasting models to make predictions about the short-term running trend of the closing price. The interval EMD decomposition algorithm is introduced in this paper, considering both high and low prices to be contained in the input and output. By analyzing the closing price, high and low prices of the stock index at the same time, the volatility of this interval time series of the index and its trend can be better captured.

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