Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression

2007 ◽  
Vol 594 (1) ◽  
pp. 101-106 ◽  
Author(s):  
Guoqing Wang ◽  
Yu-an Sun ◽  
Qingzhu Ding ◽  
Chunhong Dong ◽  
Dexue Fu ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Support Vector Regression ◽  
Simultaneous Determination ◽  
Component Analysis ◽  
Independent Component ◽  
Support Vector ◽  
Ultraviolet Spectra ◽  
Kernel Independent Component Analysis ◽  
Source Spectra

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.

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