Rainfall Forecast in Different Methods of Trend Equations by Fuzzy Time Series

D Rajan, R Sugunthakunthalambigai

Abstract


Fuzzy time series models have been put forward for Rainfall Prediction from many researchers around the globe. Fuzzy time series methods do not require any assumptions valid for classic time series approaches. The most important disadvantage of fuzzy time series approaches is that it needs subjective decisions, especially in fuzzification stage. This paper proposes a novel improvement of forecasting approach based on using first order fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical rainfall data of Trichy district. In this study reveals some feature of FTS predicting Rainfall and the results have been compared with other methods.


Keywords


Predicting Rainfall, Fuzzy Time Series; Time Variant, Fuzzy Membership Grade; Fuzzy Logical Relations, Fuzzy Relation.

Full Text:

PDF

References


Bintley, H. (1987). Time series analysis with REVEAL, Fuzzy Sets and Systems 23, 97-118.

Chen, S.M. C.C. Hsu, (2004). A new method to forecast enrollments using fuzzy time series, International Journal of Applied Sciences and Engineering 2 (3) 234–244

Chen, S.M. (1996), Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems 81 311–319.

Chen, S.M. (2002) Forecasting enrollments based on high-order fuzzy time series, Cybernetics and Systems: An International Journal 33 1–16.

Chen, S.M. J.R. Hwang, (2000) Temperature prediction using fuzzy time series, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 30 (2) 263– 275.

Dincer, N.G., Akkus¸, ¨O.,(2018). A new fuzzy time series model based on robust clustering for forecasting of air pollution. Ecol. Inform. 43, 157–164.

Fraiha Lopes, R.L., Fraiha, S.G., Gomes, H.S., Lima, V.D., Cavalcante, G.P., (2020).Application of hybrid ARIMA and artificial neural network modelling for electromagnetic propagation: an alternative to the least squares method and ITU.

Kaufmann,A and M.M. Gupta, (1988). Fuzzy Mathematical Models in Engineering and Management Sciences (NorthHolland, Amsterdam,

Song ,Q. and B.S. Chissom, (1993). Fuzzy time series and its models, Fuzzy Sets and Systems 54 269-277.

Song, Q. and B.S. Chissom, (1993). Forecasting enrollments with fuzzy time series - part I, Fuzzy Sets and Systems 54 1 9.

Song, Q. and B.S. Chissom, (1994). Forecasting enrollments with fuzzy time series - part II, Fuzzy Sets and Systems 62 1-8.

Sullivan, J. W.H. Woodall, (1994). A comparison of fuzzy forecasting and Markov modeling, Fuzzy Sets and Systems 64 279 293.

Tsaur, R.C. J.C.O. Yang, H.F. Wang, (2005). Fuzzy relation analysis in fuzzy time series model, Computer and Mathematics with Applications 49 539–548

Tsaur, R.C., (2012). A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and US dollar. Int. J. Innov. Comput., Inf. Control 8 (7), 4931–4942.

Van Tinh, N., Vu, V.V., Linh, T.T.N., (2016). A new method for forecasting enrolments combining time-variant fuzzy logical relationship groups and K-means clustering. Int. Res. J. Eng. Technol. 3 (3), 1–32.

Wang, H., Jiao, M., Tan, Y., (2016). Air quality index forecast based on fuzzy time series models. J. Resid. Sci. Technol. 13 (5),

Wang, J., Li, H., Lu, H., (2018). Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China. Appl. Soft Comput. 71, 783–799.

Wu, W. (1986) . Fuzzy reasoning and fuzzy relational equations, Fuzzy Sets and Systems 20 67 78.

Yan, Y., Li, Y., Sun, M., Wu, Z., (2019). Primary pollutants and air quality analysis for urban air in China: evidence from Shanghai. Sustainability 11 (8), 2319.

Yang, H., Zhu, Z., Li, C., Li, R., (2019). A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight. Appl. Soft Comput. 105972.

YousifAlyousifi ,Mahmod Othman , Abdullah Husin , UpakaRathnayake(2021). A new hybrid fuzzy time series model with an application to predict PM10 concentration.

Yu, H.K., (2005). Weighted fuzzy time series models for TAIEX forecasting. Phys. A: Stat. Mech. Appl. 349 (34), 609–624.

Zadeh, L.A. Fuzzy sets, Inform. and Control 8 (1965) 338-353.

Zadeh, L.A. (1973) Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Systems Man Cybernet. 3 28-44.

Zadeh, L.A. (1975) The concept of a linguistic variable and its application to approximate reasoning, parts I-3, Inform. Sci. 8:199-249; 8 ,301 357; 9 : 43-80.

Zhang, G.P., (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175.

Zhang, Y., Qu, H., Wang, W., Zhao, J., (2020). A novel fuzzy time series forecasting model based on multiple linear regression and time series clustering. Math. Probl. Eng.

Zhang, Z., Zhu, Q., (2012). Fuzzy time series forecasting based on k-means clustering. Open J. Appl. Sci. 2, 100–103.




DOI: http://dx.doi.org/10.23755/rm.v45i0.1011

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Rajan D, Sugunthakunthalambigai R

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Ratio Mathematica - Journal of Mathematics, Statistics, and Applications. ISSN 1592-7415; e-ISSN 2282-8214.