Application of Independent Component Analysis Preprocessing and Support Vector Regression in Time Series Prediction

2009 ◽  
Author(s):  
Chi-Jie Lu ◽  
Jui-Yu Wu ◽  
Tian-Shyug Lee
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Support Vector Regression ◽  
Time Series Prediction ◽  
Component Analysis ◽  
Independent Component ◽  
Support Vector

Chaotic Time Series Prediction Using a Novel Echo State Network Model with Input Reconstruction, Bayesian Ridge Regression and Independent Component Analysis

2019 ◽  
Vol 34 (06) ◽  
pp. 2051008
Author(s):  
Hoang Minh Nguyen ◽  
Gaurav Kalra ◽  
Taejoon Jun ◽  
Daeyoung Kim
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Ridge Regression ◽  
Time Series Prediction ◽  
Component Analysis ◽  
Chaotic Time Series ◽  
Independent Component ◽  
Echo State Network ◽  
Chaotic Time Series Prediction ◽  
Input Reconstruction

This paper presents a novel Echo State Network (ESN) model for chaotic time series prediction, which consists of three steps including input reconstruction, dimensionality reduction and regression. First, phase-space reconstruction is used to reconstruct the original ‘attractor’ of the input time series. Then, Independent Component Analysis (ICA) is used to identify independent components, reduce dimensionality and overcome multicollinearity problem of the reconstructed input matrix. Finally, Bayesian Ridge Regression provides accurate predictions thanks to its regularization effect to avoid over-fitting and its robustness to noise owing to its probabilistic strategy. Our experimental results show that our model significantly outperforms other ESN models in predicting both artificial and real-world chaotic time series.

scholarly journals Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Wensheng Dai ◽  
Jui-Yu Wu ◽  
Chi-Jie Lu
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Support Vector Regression ◽  
Component Analysis ◽  
Independent Component ◽  
Support Vector ◽  
Chain Store ◽  
Sales Data ◽  
Feature Extraction Method ◽  
Promising Tool

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Modeling Method Based on Smooth Support Vector Regression with Independent Component Analysis Feature Extraction

2015 ◽  
Vol 713-715 ◽  
pp. 1773-1776
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Support Vector Regression ◽  
Function Approximation ◽  
Component Analysis ◽  
Modeling Method ◽  
Independent Component ◽  
Support Vector ◽  
Better Than

Smooth Support Vector Regression (SSVR) is new modified edition of traditional support vector regression for better performance. To further improve the modeling capability of SSVR, it is necessary to take into account the feature extraction based on Independent Component Analysis (ICA) before SSVR. Simulation on the example of function approximation shows that the result of SSVR based on ICA feature extraction is better than that of SSVR without ICA preprocess.

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