Abstract
Literature reviews show that the most commonly studied fuzzy time series models for the purpose of forecasting is first order. In such approaches, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important class in time series models. In this paper, a weighted fuzzy integrated time series is proposed in order to analyze trend and seasonal data and data are taken from tourist arrivals series. The proposed approach is based on differencing concept as data preprocessing method and weighted fuzzy time series. The order of this model is determined by utilizing graphical order fuzzy relationship. Four data sets about the monthly number of tourist arrivals to Indonesia via four main gates are selected to illustrate the proposed method and compare the forecasting accuracy with classical time series models. The results of the comparison in test data show that the weighted fuzzy integrated time series produces more precise forecasted values than those classical time series models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Song, Q., Chissom, B.S.: Fuzzy Forecasting Enrollments with Fuzzy Time Series-Part 1. Fuzzy Sets and Systems 54(1), 1–9 (1993)
Song, Q., Chissom, B.S.: Fuzzy Time Series and Its Models. Fuzzy Sets and Systems 54(3), 269–277 (1993)
Liu, H.T.: An Integrated Fuzzy Time Series Forecasting System. Expert Systems with Applications 36, 10045–10053 (2009)
Egrioglu, E., Aladag, C.H., Yolcu, U., Basaran, M.A., Uslu, V.R.: A New Hybrid Approach Based on SARIMA and Partial High Order Bivariate Fuzzy Time Series Forecasting Model. Expert Systems with Applications 36, 7424–7434 (2009)
Suhartono, Lee, M.H.: A Hybrid Approach Based on Winter’s Model and Weighted Fuzzy Time Series for Forecasting Trend and Seasonal Data. Journal of Mathematics and Statistics 7(3), 177–183 (2011)
Chen, S.M.: Forecasting Enrollments Based on Fuzzy Time Series. Fuzzy Sets and Systems 81(3), 311–319 (1996)
Yu, H.K.: Weighted Fuzzy Time-Series Models for TAIEX Forecasting. Physica A: Statistical Mechanics and its Applications 349, 609–624 (2005)
Cheng, C.H., Chen, T.L., Teoh, H.J., Chiang, C.H.: Fuzzy Time Series Based on Adaptive Expectation Model for TAIEX Forecasting. Expert Systems with Applications 34(2), 1126–1132 (2008)
Lee, M.H., Suhartono: A Novel Weighted Fuzzy Time Series Model for Forecasting Seasonal Data. In: Proceeding 2nd International Conference on Mathematical Sciences, Kuala Lumpur, Malaysia, pp. 332–340 (2010)
Zhang, G.P.: Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50, 159–175 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suhartono, Lee, M.H., Javedani, H. (2011). A Weighted Fuzzy Integrated Time Series for Forecasting Tourist Arrivals. In: Abd Manaf, A., Zeki, A., Zamani, M., Chuprat, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25453-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-25453-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25452-9
Online ISBN: 978-3-642-25453-6
eBook Packages: Computer ScienceComputer Science (R0)