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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064 Volume 2 Issue 2, February 2013 www.ijsr.netHybrid ARIMA-ANFIS for Rainfall Prediction in Indonesia Ria Faulina1, Suhartono21Department of Statistics Institut Teknologi Sepuluh Nopember, Surabaya-Indonesia [email protected] 2Department of Statistics Institut Teknologi Sepuluh Nopember, Surabaya-Indonesia [email protected] mAbstract:In Indonesia, rainfall prediction is very important especially for food production. The M3-competition shows that more complicated model not always yield better forecast than simpler one. Conversely, from this competition there is a statement shows that when the various methods are being combined, the accuracy is better than the individual method. This paper proposed hybrid and ensemble model of forecasting method for ten-daily rainfall prediction based on ARIMA (Autoregressive Integrated Moving Average) and ANFIS (Adaptive Neuro Fuzzy Inference System) at six certain area in Indonesia. To find an ensemble forecast from ARIMA and ANFIS models, the averaging and stacking method was implemented. In this study, Triangular, Gaussian, and Gbell function are used as membership function in ANFIS. The best model is measured by the smallest root of mean square errors (RMSE) at testing datasets. The results show that an individual ARIMA method yields more accurate forecast in five rainfall data, whereas ensemble averaging multi model yields better forecast in one rainfall data. In general, these results in line with M3 competition results that more complicated model not always yield better forecast than simpler one. Keywords:ARIMA, ANFIS, Hybrid, Ensemble.1.Introduction Rainfall is one of factor that effects the food production in Indonesia. ARIMA is a traditional method that still used in prediction techniques, especially in climate prediction. However, there are several problems frequently arise in ARIMA modeling, i.e. the stationary of the data is often not met and determination of the order of p,d,q is often difficult. Additionally, computational intelligence methods were also used for rainfall prediction. These methods have no assumption such classical methods. ANFIS is a combination between artificial neural networks and fuzzy inference system 1. In Indonesia, statistical models which are currently developed and applied for rainfall prediction have not given adequate results. Recently, the combined method is develop in forecasting. It intended to improve forecasting accuracy. Many researches showed that combined model yielded better forecast accuracy than an individual forecasting model. The purpose of this study is to develop combining techniques for rainfall prediction in certain area in Indonesia based on ARIMA and ANFIS. Makridakis and Hibbon 2 stated that based on M3-Competition, more complicated model not always yield better forecast than simpler one. On the other hand, there is a statement shows that when the various methods are being combined, the accuracy is better than the individual method. Ten-daily rainfall data in Tlekung, Tinjumoyo, Temas, Pendem, Ngujung, and Ngaglik are used as case study. Four main forecasting methods will be developed and compared, i.e. ARIMA, ANFIS, Hybrid methods, and Ensemble methods. The best model is measured by the smallest root of mean square errors (RMSE) at testing datasets. 2.Material and Method A.ARIMA ARIMA model are introduced by Box and Jenkins in 1976. It is a linear time series models that can be used for modeling many different types of seasonal as well as non seasonal time series. The mathematics form of the ARIMA model is 34 5: tSQqtDSdSPpBByBBBB???)()()1()1)(()(?=??? (1) where )(Bp?= ppBBB????????2211)(SPB?= PSPSSBBB????????2211)(Bq?= qqBBB????????2211)(SQB?= QSQSSBBB????????2211, and S is the seasonal period, B is the backshift operator, and t? is a sequence of white noise with zero mean and constant variance. Box and Jenkins proposed four iterative steps for building ARIMA model, known as Box-Jenkins procedure, i.e. identification, parameter estimation, diagnostic checking, and forecasting. B.ANFIS ANFIS is a combination between artificial neural networks and fuzzy inference system. It has a hybrid algorithm to estimate the parameters, least square to estimate the linear parameter and error back propagation to estimate nonlinear parameter. There are five layers in ANFIS. The linear parameter will be estimated in first layer and the nonlinear parameter in fourth layer. There are some technical terms in ANFIS modeling, such as fuzzy set and fuzzy inference systems. Both terms are the basis of ANFIS modeling. Fuzzy set is the set where the membership of each element does not have clear boundaries 1. Fuzzy inference system is a method that interprets the 159International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064 Volume 2 Issue 2, February 2013 www.ijsr.netvalues in the input vector and, based on user-defined rules, assigns values to the output vector. Fuzzy set has a concept that difference with the classical set. The input space is mapped into a given weight or degree of membership through a function called membership function. It defines how each point in the input space is mapped into weights or degrees of membership between 0 and 1. In this study, we will use three types of membership functions and two types of clustering method, i.e. Triangular, Gaussian, and Gbell membership function, grid partition and fuzzy cluster mean as clustering methods. The input space will be mapped into 2 and 3 clusters. Figure 1 is an example of ANFIS structure with two inputs and two rules. Figure 1. Architecture of ANFIS with two inputs and two rules C.Hybrid Model Hybrid model is a forecasting technique that combined other individual methods. In this method, there are two level procedure to get the forecasting value, i.e. linear model on first level and nonlinear model on second level. Zhang 6 used hybrid ARIMA-ANN where ARIMA as linear model and ANN as nonlinear model. It purposed to both of them can hold linear and nonlinear pattern from the datasets. NtLtYt+= (2) where Lt is linear component and Nt is nonlinear component. Firstly, we make the ARIMA model for linear component, and then residual of linear component will contain nonlinear relationship. For example, teis residual notation at t from linear model? LtYtetˆ?= (3) and the mathematics equation from nonlinear model where is in this study we use ANFIS model is,: tntttteeefe?+=???),…,,(21 (4) f is nonlinear function and t?is random error. We can write the mathematics equation from hybrid model as : NtLtYtˆˆˆ+= (5) dengan Ntˆ is the forecast value from equation (4). D.Ensemble Model Ensemble forecasting is a forecasting technique that combines several outputs of forecasting methods. Recent studies have shown that the robustness and reliability of an combining several models into an ensemble 7-12. In ensemble model, there are two methods usually be used for combining the difference outputs from membership ensemble, i.e. averaging and stacking 11. Figure 2 shows a review of the two methods in ensemble model: Figure 2. Architecture of ensemble In averaging method, the output of the ensemble is obtained by computing the mean of the output of the member networks. Assume that N is the number of individual ANFIS members in an ensemble, the combination function (f) is: miyfykii,…,2,1,)ˆ(ˆ==(6) where iyˆ is a forecast value of the instance i obtained from the kth network and the form of the function f is ?==NkkiyNkiyf1ˆ1)ˆ((7) The averaging approach is easy, and it has been shown to be an effective approach to improve the performance of the individual forecasting model like Neural Network and ANFIS 13. Stacking is a general method with the combination of a higher-level model and the lower-level models with the purpose of achieve a greater predictive accuracy. Breiman 14 suggested minimizing the function G that can give better generalization the model, is ???====>?????????=NkkkmiNkkikiccycyG12111,0,ˆ. (8) The coefficients ???,???,…,??? in (8) are predicted in order to create the final output of the ensemble: miycykiNkki,…,2,1,ˆˆ1==?= (9) 3.Result and Discussion Six rainfall data from January 1996 until June at six area in Indonesia, i.e. Tlekung, Tinjumoyo, Temas, Pendem, Ngujung, and Ngaglik are used as case study. The data are divided in two parts, i.e. training and testing datasets. Rainfalls data from January 2011–June 2012 are used as testing datasets. Time series plot for each datasets in same graph is shown in Figure 3. 160International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064 Volume 2 Issue 2, February 2013 www.ijsr.net56052048044040036032028024020016012080401403020100Index(t)TLEKUNGTINJUMOYOTEMASPENDEMNGUJUNGNGAGLIKFigure 3. Time series plot of six rainfall data Figure 3 shows that the six datasets have same pattern. There are some extreme values in January 1996 until June 2012. Four methods are used to forecast the six rainfall data, i.e. ARIMA and ANFIS as individual methods, hybrid and ensemble model as combined methods. Accuracy of data is measured by root mean square error for each method. The best model is chosen to predict rainfall for each area based on smallest RMSE. For each dataset, ARIMA modeling is done by implementing Box-Jenkins procedure, beginning with identification, parameter estimation, diagnostic checking, and forecasting. All data are not stationary. Consequently, differencing is done to determine the order of ARIMA. Identification step based on autocorrelation function (ACF) and partial autocorrelation function (PACF) of each data yield more than one order of tentative ARIMA. The ARIMA models for each area are shown in Table 1. They have significant parameter and white noise process but normal distribution of residual didn’t meet. It happened because there are several extreme values in data. There is a solution if normal distribution of residual didn’t meet in ARIMA model, such as entering the outlier to model (ARIMAX). Table 1: ARIMA model of rainfall data for each area Area Model Tlekung ARIMA(0,0,0)(0,1,1)36 ARIMA((29),0,(29))(0,1,1) 36Tinjumoyo ARIMA(1,0,1)(0,1,1) 36ARIMA((5),0,1)(0,1,1) 36Temas ARIMA(1,0,1)(0,1,1) 36ARIMA(0,0,0)(0,1,1) 36ARIMA(0,0,1)(0,1,1) 36Pendem ARIMA(0,0,0)(0,1,1) 36ARIMA(1,0,0)(0,1,1) 36ARIMA(0,0,1)(0,1,1) 36Ngujung ARIMA((2),0,2)(0,1,1) 36ARIMA(1,0,1)(0,1,1) 36ARIMA(1,0,0)(0,1,1) 36Ngaglik ARIMA(1,0,0)(0,1,1) 36ARIMA(0,0,1)(0,1,1) 36To get ANFIS model, we must determine the input and output target. We use significant lag based on the ARIMA models as input variable. For example, there are two kind input variable in Tlekung, i.e. lag 36 derived from model one, and lag 29, 36, 65 from model two. Table 2 : The Best Model for Each Methods in Six Area Model RMSE in-sample RMSE testing Tlekung : ARIMA(0,0,0)(0,1,1)364,358 5,177 ARIMAX(0,0,0)(0,1,1)362,481 5,474 ANFIS_3input_2cluster_FCM 4,477 5,526 HybridARIMA-ANFIS 4,217 5,243 HybridARIMAX-ANFIS 2,395 5,545 Ensemble Multi Model Averaging 3,382 5,128* Tinjumoyo : ARIMA(1,0,1)(0,1,1)365,212 4,905* ARIMAX(0,0,1)(0,1,1) 362,713 5,997 ANFIS_1input_2cluster_Gbell_GP 5,098 5,396 HybridARIMA-ANFIS 4,870 4,967 HybridARIMAX-ANFIS 2,604 6,01 Ensemble single model hibrida averaging 3,736 5,096 Temas : ARIMA(0,0,0)(0,1,1)364,847 4,442* ARIMAX(0,0,(1,4,5,8,10,15))(0,1,1)361,784 5,872 ANFIS_1input_2cluster_Gbell_GP 4,909 5,239 Hybrid ARIMA-ANFIS 4,868 4,698 Hybrid ARIMAX-ANFIS 1,819 5,864 Ensemble single model ARIMA averaging 3,781 4,696 Pendem : ARIMA(0,0,1)(0,1,1)364,852 4,067* ARIMAX(0,0,(1,31))(0,1,1)363,156 4,613 ANFIS_1input_3cluster_FCM 4,869 5,258 Hybrid ARIMA-ANFIS 4,834 4,144 Hybrid ARIMAX-ANFIS 3,179 4,64 Ensemble single model ARIMA averaging 4,188 4,138 Ngujung : ARIMA(1,0,0)(0,1,1)36 4,513 4,593* ARIMAX(1,0,0)(0,1,1)361,706 5,678 ANFIS_1input_2cluster_Trianguler_GP 4,575 5,671 Hybrid ARIMA-ANFIS 4,477 4,912 Hybrid ARIMAX-ANFIS 1,717 5,662 Ensemble single model ARIMA averaging 3,551 4,785 Ngaglik : ARIMA(0,0,1)(0,1,1)364,726 4,192* ARIMAX(0,0,1)(0,1,1)362,2 5,148 ANFIS_1input_2cluster_Trianguler_GP 4,758 4,728 Hybrid ARIMA-ANFIS 4,617 4,462 Hybrid ARIMAX-ANFIS 2,175 5,044 Ensemble single model ARIMA averaging 3,542 4,395 161International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064 Volume 2 Issue 2, February 2013 www.ijsr.netTable 2 is the result of forecasting accuracy for each method. To get the best prediction, we use RMSE from testing datasets. Table 2 show that the best model for predict ten daily rainfall for Tlekung is combination methods, ensemble multi model averaging. But for other area, ARIMA is the best model for predict the rainfall although residual form this model didn’t have normal distribution. Hybrid method not always provides better accuracy than the initial model. 4.Conclusion The rainfall pattern from each area is almost same but the result model is different. The best model for predict ten daily rainfall for Tlekung is combination methods, ensemble multi model averaging. But for other area, ARIMA is the best model for predict the rainfall. It proves that more complex methods are sometimes no better than a simple method (M3-Competition). But sometimes, combination from various methods can increase the accuracy. References 1J.-S.R. Jang, C.-T. Sun, and E. 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Author Profile Ria Faulina is Master Student in Department of Statistics from Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. She has graduated from bachelor degree of Statistics Institut Teknologi Sepuluh Nopember Surabaya in 2011. She continued to Magister of Statistics in 2011 until now. She has focused her study in forecasting. Mr. Suhartono received her Bachelor degree in Statistics from Institut Teknologi Sepuluh Nopember Surabaya-Indonesia, Master in UMIST, Manchester and Doctor in Universitas Gadjah Mada Indonesia. Recently, he is a lecturer in Department of Statistics Institut Teknologi Sepuluh Nopember. Surabaya-Indonesia. He has focused her study in time series forecasting, econometrics, neural network, and spatial time series model.162

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