How Do You Make A Time Series Sing Like a Choir? Extracting Embedded Frequencies from Economic and Financial Time Series using Empirical Mode Decomposition

2012 ◽  
Vol 16 (5) ◽  
Author(s):  
Patrick M. Crowley
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Financial Time Series ◽  
Financial Time ◽  
Mode Decomposition

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.

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 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.

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.

An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting

2016 ◽  
Vol 25 (02) ◽  
pp. 1650005 ◽  
Author(s):  
Heng-Li Yang ◽  
Han-Chou Lin
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Linear Part ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Nonlinear Part ◽  
Financial Time ◽  
Stock Indices ◽  
Mode Decomposition ◽  
Original Time

Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.

Sign in / Sign up

Export Citation Format

Share Document