The Forecasting Of Labour Force Participation And The Unemployment Rate In Poland And Turkey Using Fuzzy Time Series Methods

YOLCU U. , Bas E.

COMPARATIVE ECONOMIC RESEARCH-CENTRAL AND EASTERN EUROPE, vol.19, no.2, pp.5-25, 2016 (Journal Indexed in ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 2
  • Publication Date: 2016
  • Doi Number: 10.1515/cer-2016-0010
  • Page Numbers: pp.5-25
  • Keywords: fuzzy time series, forecasting, labour force participation, unemployment, ARTIFICIAL NEURAL-NETWORKS, GENETIC ALGORITHMS, ENROLLMENTS, INTERVALS, MODEL, LENGTH, OPTIMIZATION, TAIEX


Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965) was first introduced by Song and Chissom (1993). Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods.