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

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 CPP
n,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 CPP
n,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.