Please use this identifier to cite or link to this item: https://publication.npru.ac.th/jspui/handle/123456789/887
Title: A Comparison of Forecasting Techniques Efficiency for Financial Stress Data using Data Mining Techniques
Authors: Kaewwichit, Pichet
Kawila, Udomsak
Lukkananuruk, Nitima
Hengpraphorm, Kairung
Hengpraphorm, Supojn
Keywords: Linear Regression
Neural Network
Support Vector Machine
Issue Date: 9-Jul-2020
Publisher: Nakhon Pathom Rajabhat University
Abstract: The purpose of this research is to study and compare the forecasting techniques which are suitable with the financial stress data. In this research, three techniques including linear regression, Artificial Neural Networks, and Support Vector Machine, have been selected. In order to find the best of effectiveness for the financial stress data forecasting. The result shows that Support Vector Machin gives the best performance in terms of the square root of the mean squared error (2.58). The second one is the artificial neural network that gives the square root of the mean squared error = 3.34. Linear regression provides the lowest performance (the square root of the mean square error = 82.49).
URI: https://publication.npru.ac.th/jspui/handle/123456789/887
Appears in Collections:Proceedings of the 12th NPRU National Academic Conference



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