Using the small data set learning for the monitor indicator forecast

Authors:
Fengming Michael Chang
Affiliations:
Department of Marketing and Distribution Management, Tungfang Institute of Technology, Kaohsiung, Taiwan
Venue:
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Year:
2006

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Abstract

It is hard to have good accuracy for economic forecast because most of the machine learning methods rely on large amounts of data to make predication and the historical data is not enough. Even though for ten years' annual data, it has only ten data. This article tries to improve the economic forecast accuracy using a Mega-fuzzification method for small data set learning based on neuro-fuzzy. Methods used include virtual data concept, data continualization, data effect estimation, fuzzy neural network, and data range external expansion. A case of Taiwan's monitor indicator forecast is presented also in this study. The results show that the proposed method can indeed improve the accuracy of the economic forecast.