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Time series forecasting based on a neural network with weighted fuzzy membership functions

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2 Author(s)
Sang-Hong Lee ; IT Coll., Kyungwon Univ., Seongnam, South Korea ; Lim, J.S.

This paper proposes time series forecasting using a new feature selection method based on the non-overlap area distribution measurement method and Takagi's and Sugeno's fuzzy model. The non-overlap area distribution measurement method selects the minimum number of 4 input features with the highest performance result from 12 initial input features by removing the worst input features one by one. This paper proposes CPPn,m (Current Price Position on day n: percentage of the difference between the price on day n and the moving average of the past m days' prices from day n-1) as a new technical indicator. The performance result improves by from 58.35% to 58.86% when CPPn,5 is added to the minimum number of 4 input features that are selected by the non-overlap area distribution measurement method as a new input feature.

Published in:

Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on  (Volume:2 )

Date of Conference:

26-28 Feb. 2010