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

Ufuk Yolcu; Eren Bas

doi:10.1515/cer-2016-0010

Authors

Ufuk Yolcu Ankara University, Faculty of Sciences, Department of Statistics
Eren Bas Giresun University, Faculty of Arts and Sciences, Department of Statistics

DOI:

https://doi.org/10.1515/cer-2016-0010

Keywords:

fuzzy time series, forecasting, labour force participation, unemployment

Abstract

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.

Downloads

Download data is not yet available.

References

Aladag C.H., Basaran M.A., Egrioglu E., Yolcu U. and Uslu V.R. (2009), Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations, ʻExpert Systems with Applicationsʼ, 36, 4228–4231.
Google Scholar

Alpaslan F., Cagcag O., Aladag C.H., Yolcu U., and Egrioglu E. (2012), A novel seasonal fuzzy time series method, ʻHacettepe Journal of Mathematics and Statisticsʼ, 41(3), 375–385.
Google Scholar

Bezdek J.C. (1981), Pattern recognition with fuzzy objective function algorithms, Plenum Press., New York.
Google Scholar

Cagcag Yolcu O. (2013), A hybrid fuzzy time series approach based on fuzzy clustering and artificial neural network with single multiplicative neuron model, ʻMathematical Problems in Engineeringʼ, Vol. 2013, Article ID 560472, 9 pages.
Google Scholar

Chen S.M. (1996), Forecasting enrollments based on fuzzy time-series, ʻFuzzy Sets and Systemsʼ, 81, 311–319.
Google Scholar

Chen S.M. (2002), Forecasting enrollments based on high order fuzzy time series, ʻCybernetics and Systemsʼ 33, 1–16.
Google Scholar

Chen S.M. and Chung N.Y. (2006), Forecasting enrolments using high order fuzzy time series and genetic algorithms, ʻInternational Journal of Intelligent Systemsʼ, 21, 485–501.
Google Scholar

Chen S.M. and Chen C.D. (2011), TAIEX forecasting based on fuzzy time series and fuzzy variation groups, ʻIEEE Transactions on Fuzzy Systemsʼ, vol. 19 No.1.
Google Scholar

Cheng C-H., Cheng G-W. and Wang J-W. (2008), Multi-attribute fuzzy time series method based on fuzzy clustering, ʻExpert Systems with Applicationsʼ, 34, 1235–1242.
Google Scholar

Davari S., Zarandi M.H.F. and Turksen I.B. (2009), An Improved fuzzy time series forecasting model based on particle swarm intervalization, The 28th North American Fuzzy Information Processing Society Annual Conferences (NAFIPS 2009), Cincinnati, Ohio, USA, June 14–17.
Google Scholar

Egrioglu E., Aladag C.H., Yolcu U., Uslu V.R. and Basaran M.A. (2009), A new approach based on artificial neural networks for high order multivariate fuzzy time series,ʻExpert Systems with Applicationsʼ, 36, 10589–10594.
Google Scholar

Egrioglu E., Aladag C.H., Yolcu U., Basaran M.A. and Uslu V.R. (2009), A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model, ʻExpert Systems with Applicationsʼ, 36, 7424–7434.
Google Scholar

Egrioglu E., Uslu V.R., Yolcu U., Basaran M.A. and Aladag C.H. (2009) A new approach based on artificial neural networks for high order bivariate fuzzy time series, J.Mehnen et al. (Eds.): Applications of Soft Computing, AISC 58, Springer-Verlag Berlin Heidelberg, 265–273.
Google Scholar

Egrioglu E., Aladag C.H., Yolcu U., Uslu V.R. and Basaran M.A. (2010), Finding an optimal interval length in high order fuzzy time series, ʻExpert Systems with Applicationsʼ 37, 5052–5055.
Google Scholar

Egrioglu E., Aladag C.H., Basaran M.A., Uslu V.R. and Yolcu U. (2011), A new approach based on the optimization of the length of intervals in fuzzy time series, ʻJournal of Intelligent and Fuzzy Systemsʼ, 22, 15–19.
Google Scholar

Hsu L-Y., Horng S-J., Kao T-W., Chen Y-H., Run R-S., Chen R-J., Lai J-L. and Kuo I-H. (2010), Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques, ʻExpert Systems with applicationsʼ, 37, 2756–2770
Google Scholar

Huarng K. (2001), Effective length of intervals to improve forecasting in fuzzy time-series, ʻFuzzy Sets and Systemsʼ, 123, 387–394.
Google Scholar

Huarng K. and Yu T.H.-K. (2006), Ratio-based lengths of intervals to improve fuzzy time series forecasting, ʻIEEE Transactions on Systems, Man,, and Cybernetics-Part B: Cyberneticsʼ, 36, 328–340.
Google Scholar

Huarng K. and Yu T.H.-K. (2006b), The application of neural networks to forecast fuzzy time series, ʻPhysica Aʼ, 363, 481–491.
Google Scholar

Huarng K. and Yu T.H.-K. and Hsu Y.W. (2007), A multivariate heuristic model for fuzzy time-series forecasting, ʻIEEE Trans. Syst., Man, Cybern. B, Cybernʼ, 37 (4), 836–846.
Google Scholar

Jilani T.A. and Burney S.M.A. (2007), M-factor high order fuzzy time series forecasting for road accident data: Analysis and design of intelligent systems using soft computing techniques, ʻAdvances in Soft Computingʼ, 41, 246–254.
Google Scholar

Jilani T.A. and Burney S.M.A. (2008), Multivariate stochastic fuzzy forecasting models, ʻExpert Systems with Applicationsʼ, 35(3), 691–700.
Google Scholar

Jilani T.A., Burney S.M.A. and Ardil C. (2007), Multivariate high order fuzzy time series forecasting for car road accidents, ʻInternational Journal of Computational Intelligenceʼ, 4(1), 15–20.
Google Scholar

Kuo I-H., Horng S-J., Kao T-W., Lin T.-L., Lee C.-L. and Pan Y. (2009), An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization, ʻExpert Systems with Applicationsʼ, 36, 6108–6117.
Google Scholar

Kuo I-H., Horng S-J., Chen Y-H., Run R-S., Kao T-W., Chen R-J., Lai J-L. and Lin T-L. (2010), Forecasting TAIFEX based on fuzzy time series and particle swarm optimization, ʻExpert Systems with Applicationsʼ, 37, 1494–1502.
Google Scholar

Lee L.W., Wang L.H., Chen S.M. and Leu Y.H. (2006), Handling forecasting problems based on two factor high-order fuzzy time series, ʻIEEE Trans. on Fuzzy Systemsʼ, 14 No:3, 468–477.
Google Scholar

Lee L.W., Wang L.H. and Chen S.M. (2007), Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms, ʻExpert Systems with Applicationsʼ, 33, 539–550.
Google Scholar

Lee L.W., Wang L.H. and Chen S.M. (2008), Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques, ʻExpert Systems with Applicationsʼ, 34, 328–336.
Google Scholar

Li S-T., Cheng Y-C. and Lin S-Y. (2008), A FCM-based deterministic forecasting model for fuzzy time series, ʻComputers and Mathematics with Applicationsʼ, 56, 3052–3063.
Google Scholar

Park J-I., Lee D-J., Song C-K. and Chun M-G. (2010), TAIFEX and KOSPI 200 forecasting based on two factors high order fuzzy time series and particle swarm optimization, ʻExpert Systems with Applicationsʼ, 37, 959–967.
Google Scholar

Song Q. and Chissom B.S. (1993), Fuzzy time series and its models, ʻFuzzy Sets and Systemsʼ, 54, 269–277.
Google Scholar

Song Q. and Chissom B.S. (1993), Forecasting enrollments with fuzzy time series-Part I., ʻFuzzy Sets and Systemsʼ, 54, 1–10.
Google Scholar

Song Q. and Chissom B.S. (1994), Forecasting enrollments with fuzzy time series-Part II., ʻFuzzy Sets and Systemsʼ, 62, 1–8.
Google Scholar

Yolcu U., Egrioglu E., Uslu V.R., Basaran M.A. and Aladag C.H. (2009), A new approach for determining the length of intervals for fuzzy time series, ʻApplied Soft Computingʼ, 9, 647–651.
Google Scholar

Yolcu U., Cagcag O., Aladag C.H., and Egrioglu E. (2014), An enhanced fuzzy time series forecasting method based on artificial bee, ʻJournal of Intelligent & Fuzzy Systemsʼ, 26 (6), 2627–2637.
Google Scholar

Yu T.H-K. and Huarng K. (2008), A bivariate fuzzy time series model to forecast TAIEX, ʻExpert Systems with Applicationsʼ, 34, 2945–2952.
Google Scholar

Yu T.H-K. and Huarng K. (2010), A neural network- based fuzzy time series model to improve forecasting, ʻExpert Systems with Applicationsʼ, 37, 3366–3372.
Google Scholar

Zadeh L.A. (1965), Fuzzy Sets, Inform and Control, 8, 338–353.
Google Scholar

Zurada J.M. (1992), Introduction of artificial neural systems, St, Paul: West Publishing.
Google Scholar

Zhang G., Patuwo B.E. and Hu Y.M. (1998), Forecasting with artificial neural networks: the state of the art, ʻInternational Journal of Forecastingʼ, 14, 35–62.
Google Scholar