This paper proposes a new forecasting model based on the neural network
with weighted fuzzy membership functions (NEWFM) concerning forecasting
of turning points in the business cycle by the composite index. NEWFM is a
new model of neural networks to improve forecasting accuracy by using self
adaptive weighted fuzzy membership functions. The locations and weights of
the membership functions are adaptively trained, and then the fuzzy
membership functions are combined by the bounded sum. To simplify the
forecasting processes, the non-overlap area distribution measurement
method is applied to select important features by deleting less important
inputs. The implementation of the NEWFM demonstrates an excellent
capability in the field of business cycle analysis.