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Cao, L.J. and Tay, F.E.H. (2003) Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Transaction on Neural Networks, 14, 1506-1518.
https://doi.org/10.1109/TNN.2003.820556

has been cited by the following article:

• TITLE: A New Approach of Time Series Variation Based on Power Links and Field Association Words

  AUTHORS: Zohair S. Malki, El-Sayed Atlam, Talal H. Noor, Ahmad Reda Alzighaibi, Ghada Elmarhomy, Abdallah A. Mohamed

  KEYWORDS: Information Retrieval, Time Changing, Power Link Analysis, Field Association Words, Decision Tree

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.3, March 14, 2020

  ABSTRACT: This paper has proposed a new methodology extracting stability classes of field association words depending on automatically power link analysis to enhance the precision of decision tree. In this paper, we have studied the effects of the time variation based on the frequencies of specific words called field association words that connected to documents using power link in a specific period. The stability classes have referred to the popularity of field association words based on the change of time in a given period. The new approach has evaluated by conducting experiments simulating results of 1575 files (about 5.16 MB). Based on these experiments, it has turned out that, the F-measure for ascending, stable and descending classes have achieved 93.6%, 99.8% and 75.7%, respectively. These results mean that F-measure was increasing by 12%, 4% and 34% than traditional methods because of the power link analysis.