A NEW METHOD FOR POPULATION FORECASTING BASED ON FUZZY TIME SERIES WITH HIGHER FORECAST ACCURACY RATE

The Time-Series models have been used to make predictions in whether forecasting, academic enrollments, etc. Song & Chissom introduced the concept of fuzzy time series in 1993. Over the past 19 years, many fuzzy time series methods have been proposed for population forecasting. But the forecasting accuracy rate of the existing methods is not good enough. These methods have either used actual population or difference of population as the universe of discourse. And either used frequency density based partitioning or nature- ratio based partitioning. In this paper, we proposed a method based on fuzzy time series, which gives the higher forecasting accuracy rate than the existing methods. The proposed method used the percentage change as the universe of discourse and mean based partitioning. To illustrate the forecasting process, the historical population of Azerbaijan is used.