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 An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

MODELING MEMORY OF ECONOMIC AND FINANCIAL TIME SERIES

2005 ◽  
Vol 50 (01) ◽  
pp. 1-8 ◽  
Author(s):  
PETER M. ROBINSON
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Statistical Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Nonlinear Models ◽  
Series Data ◽  
Financial Variables ◽  
Financial Time ◽  
Linear And Nonlinear

Much time series data are recorded on economic and financial variables. Statistical modeling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of "memory", or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are discussed.

scholarly journals Empirical Mode Decomposition Combined with Local Linear Quantile Regression for Automatic Boundary Correction

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Abobaker M. Jaber ◽  
Mohd Tahir Ismail ◽  
Alssaidi M. Altaher
Keyword(s):  
Time Series ◽  
Quantile Regression ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Series Data ◽  
Second Stage ◽  
Mode Decomposition ◽  
Partial Data ◽  
Final Estimate ◽  
Practical Performance

Empirical mode decomposition (EMD) is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a new two-stage method to automatically decrease the boundary effects present in EMD. At the first stage, local polynomial quantile regression (LLQ) is applied to provide an efficient description of the corrupted and noisy data. The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD.

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