INTRODUCTION
In the last few decades, many forecasting models have been developed (Markridakis, 1982). Which among them, the AutoRegressive Integrated Moving Average (ARIMA) model has been highly popularized, widely used and successfully applied not only in economic time series forecasting, but also as a promising tool for modeling the empirical dependencies between successive times and failures (Ho and Xie, 1998). Recently, it is well documented that many economic time series observations are nonlinear while, a linear correlation structure is assumed among the time series values therefore, the ARIMA model can not capture nonlinear patterns and, approximation of linear models to complex realworld problem is not always satisfactory. While nonparametric nonlinear models estimated by various methods such as Artificial Intelligence (AI), can fit a data base much better than linear models and it has been observed that linear models, often forecast poorly which limits their appeal in applied setting (Racine, 2001).
Artificial Intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and illdefined problems (Kalogirou, 2003). They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and once trained can perform prediction and generalization at high speed (Kamwa et al., 1996). They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing and social/psychological sciences. AI systems comprise areas like expert systems, ANNs, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques (Kamwa et al., 1996).
In this study, we compare the accuracy of new Adaptive NeuroFuzzy Inference System (ANFIS) model as the nonlinear model with the ARIMA as the linear model for forecasting 1, 2 and 4 weeks ahead of weekly Iran’s poultry retail price using the forecast evaluation criteria include; R^{2}, MAD and RMSE.
MATERIALS AND METHODS
AutoRegressive Integrated Moving Average (ARIMA) Model
Introduced by Box and Jenkins (1978), in the last few decades the ARIMA
model has been one of the most popular approaches of linear time series forecasting
methods. An ARIMA process is a mathematical model used for forecasting. One
of the attractive features of the BoxJenkins approach to forecasting is that
ARIMA processes are a very rich class of possible models and it is usually possible
to find a process which provides an adequate description to the data. The original
BoxJenkins modeling procedure involved an iterative threestage process of
model selection, parameter estimation and model checking. Recent explanations
of the process (Makridakis et al., 1998) often add a preliminary stage
of data preparation and a final stage of model application (or forecasting).
The ARIMA (p, d, q) model is as follow:
where, y_{t} and e_{t} are the target value and random error at time t, respectively, φ_{i}(i = 1, 2,...,p) and θ_{j}(i = 1, 2,...,q) are model parameters, p and q are integers and often referred to as orders of autoregressive and moving average polynomials.
Adaptive NeuroFuzzy Inference System (ANFIS)
Many economic time series observations are nonlinear while, a linear correlation
structure is assumed among the time series values therefore, the ARIMA model
can not capture nonlinear patterns and, approximation of linear models to complex
realworld problem is not always satisfactory. Therefore, in this section the
ANFIS nonlinear model has been introduced as follow:
Adaptive NeuroFuzzy Inference System (ANFIS) is a neurofuzzy system. It has a feedforward neural network structure where each layer is a neurofuzzy system component (Fig. 1).
It simulates TSK (Takagi–Sugeno–Kang) fuzzy rule of type3 where the consequent part of the rule is a linear combination of input variables and a constant. The final output of the system is the weighted average of each rule’s output (Sugeno and Kang, 1998). The form of the type3 rule simulated in the system is as follows:

Fig. 1: 
The scheme of adaptive neural fuzzy inference system 
If x_{1} is A_{1} and x_{2} is A_{2} and...
and x_{p} is A_{p
}Then, y = c_{0}+ c_{1} x_{1}+ c_{2}x_{2}+…+
c_{p} x_{p}
where, x_{1} and x_{2} are the input variables, A_{1} and A_{2} are the membership functions, y is the output variable and c_{0}, c_{1} and c_{2} are the consequent parameters. The neural network structure contains six layers.
• 
Layer 0 is the input layer. It has n nodes where n is the
number of inputs to the system. 
• 
The fuzzy part of ANFIS is mathematically incorporated in
the form of Membership Functions (MFs). A
membership function can be any continuous and piecewise differentiable function
that transforms the input value x into a membership degree, that is to say
a value between 0 and 1. The most widely applied membership function is
the generalized bell (gbell MF), which is described by the three parameters,
a, b and c (Eq. 2). Therefore, layer 1 is the fuzzification
layer in which each node represents a membership value to a linguistic term
as a Gaussian function with the mean: 
where, a_{i}, b_{i} and c_{i} are parameters of the function. These are adaptive parameters. Their values are adapted by means of the backpropagation algorithm during the learning stage. As the values of the parameters change, the membership function of the linguistic term, A_{i} changes. These parameters are called premise parameters. In that layer there exist nxp nodes where n is the number of input variables and p is the number of membership functions. For example, if size is an input variable and there exist two linguistic values for size which are small and large then two nodes are kept in the first layer and they denote the membership values of input variable size to the linguistic values small and large.
• 
Each node in Layer 2 provides the strength of the rule by
means of multiplication operator. It performs and operation 
Every node in this layer computes the multiplication of the input values and
gives the product as the output as in the above equation. The membership values
represented by
are multiplied in order to find the firing strength of a rule where the variable
x_{0} has linguistic value A_{i} and x_{1} has linguistic
value B_{i} in the antecedent part of rule l.
There are p^{n} nodes denoting the number of rules in layer 2. Each node represents the antecedent part of the rule. If there are two variables in the system namely x_{1} and x_{2} that can take two fuzzy linguistic values, small and large, there exist four rules in the system whose antecedent parts are as follows:
If x_{1} is small and x_{2} is small
If x_{1} is small and x_{2} is large
If x_{1} is large and x_{2} is small
If x_{1} is large and x^{2} is large
• 
Layer 3 is the normalization layer which normalizes the strength
of all rules according to the equation: 
where, w_{i} is the firing strength of the ith rule which is computed in layer 2. Node i computes the ratio of the ith rule’s firing strength to the sum of all rules’ firing strengths. There are p^{n} nodes in this layer.
• 
Layer 4 is a layer of adaptive nodes. Every node in this layer
computes a linear function where the function coefficients are adapted by
using the error function of the multilayer feedforward neural network 
where, p^{i}’s are the parameters where i = n+1 and n is the number
of inputs to the system (i.e., number of nodes in Layer 0). In this example,
since there exist two variables (x_{1} and x_{2}), there are
three parameters p_{0}, p_{1} and p_{2}) in layer 4
and
is the output of layer 3. The parameters are updated by a learning step. Kalman
filtering based on leastsquares approximation and backpropagation algorithm
is used as the learning step.
• 
Layer 5 is the output layer whose function is the summation
of the net outputs of the nodes in layer 4. The output is computed as: 
where,
is the output of node i in layer 4. It denotes the consequent part of rule i.
The overall output of the neurofuzzy system is the summation of the rule consequences.
The ANFIS uses a hybrid learning algorithm in order to train the network. For the parameters in the layer 1, backpropagation algorithm is used. For training the parameters in the layer 4, a variation of leastsquares approximation or backpropagation algorithm is used.
DATA DESCRIPTION AND FORECAST EVALUATION CRITERIA
For the exercise which is follows, we modeled the Iran’s poultry retail
price as a function of past prices. Clearly, this has the shortcoming that our
models are somewhat naive from the perspective of theoretical macroeconomics.
However, there is a large body of literature in economics suggesting that very
parsimonious models, such ARIMA model, perform better than more complex models,
at least from the perspective of forecasting (Chen et al., 2001). The
study was carried out in Iran through the 2008:52008:9. We obtained the weekly
poultry retail price time series of Iran for the period 2002:32007:12 from
the website of Iran State Livestock Affairs Logistics (http://www.IranSLAL.com).
Also, we consider the period 2003:32007:3 (70% of total observations) and 2007:3200812
(30% of total observations) for training and testing of all models, respectively.
The Iran’s weekly poultry retail price changes during this period have
been shown in Fig. 2.
Figure 2 shows that these changes are swinger with the average and standard error of 15841.61 and 2405.79, respectively.

Fig. 2: 
Iran’s weekly poultry retail price changes during 2003:3
to 2008:12 
Table 1: 
Forecast evaluation criteria 

y_{t},
and n are the actual value, output value and the number of observations,
respectively 
Beside, in order to evaluate and compare the forecasting performance, it is necessary to introduce forecasting evaluation criteria. In this research, three criteria include; R^{2}, Mean Absolute Deviations (MAD) and Root Mean Square Error (RMSE) are used. Table 1 shows the R^{2}, MAD and RMSE formulation.
RESULTS AND DISCUSSION
Linear and NonLinear Models Performance to Poultry Price Forecasting
Concerning the application of AI systems to time series forecasting, there
have been mixed reviews. For instance, Lapedes and Farber (1987) reported that
simple neural networks can outperform conventional methods, sometimes by orders
of magnitude. Sharda and patil (1990) conducted a forecasting competition between
neural network models and traditional forecasting technique (namely the BoxJenkins
method) using 75 time series of various natures. They concluded that simple
neural nets could forecast about as well as the BoxJenkins forecasting system.
Wu (1995) conducts a comparative study between neural networks and ARIMA models
in forecasting the Taiwan/US dollar exchange rate. His findings show that neural
networks produce significantly better results than the best ARIMA models in
both onestepahead and sixstepahead forecasting. Similarly, Hann and Steurer
(1996), Zhang and Hu (1998) find results in favor of neural network. Gencay
(1999) compares the performance of neural network with those of random walk
and Generalized AutoRegressive Conditional Hetroskedastic (GARCH) models in
forecasting daily spot exchange rates for the British pound, Deutsche mark,
French franc, Japanese yen and the Swiss Franc. He finds that forecasts generated
by neural network are superior to those of random walk and GARCH models. Ince
and Trafalis (2006) proposed a two stage forecasting model which incorporates
parametric techniques such as AutoRegressive Integrated Moving Average (ARIMA),
Vector AutoRegressive (VAR) and cointegration techniques and nonparametric
techniques such as Support Vector Regression (SVR) and Artificial Neural Networks
(ANN) for exchange rate prediction. Comparison of these models showed that input
selection is very important. Furthermore, findings showed that the SVR technique
outperforms the ANN for two input selection methods.
Haofei et al. (2007) introduced a MultiStage Optimization Approach (MSOA) used in backpropagation algorithm for training neural network to forecast the Chinese food grain price. Their empirical results showed that MSOA overcomes the weakness of conventional BP algorithm to some extend. Furthermore the neural network based on MSOA can improve the forecasting performance significantly in terms of the error and directional evaluation measurements.
In ARIMA model, we identified the degree of integration (d) by augmented DickeyFuller and Schwarz criteria and the degree of autoregessive (p) and moving average (q) by loglikelihood function and akaike information criterion. In ANFIS, the hybrid learning algorithm is used to identify the membership function parameters of singleoutput, Sugeno type Fuzzy Inference Systems (FIS). A combination of leastsquares and backpropagation gradient descent methods are used for training FIS membership function parameters to model a given set of input/output data. In Genfis1 which, Generates an initial Sugenotype FIS for ANFIS training using a grid partition the gauss and gauss2 types of membership function are used for each input and linear membership function is used for output. Also, 3 and 4 numbers of membership functions are used for each input. The forecasting performance of Iran’s poultry retail price obtained by the ARIMA and ANFIS models has been shown in Table 2.
Figure 3ac show the outsample fitness of the best designed structures of ARIMA and ANFIS models for forecasting 1, 2 and 4 weeks ahead of Iran’s poultry retail price in comparison with the actual observations. Table 2 shows the quantity of evaluation criterions correspond to the best ARIM and ANFIS structures for forecasting the considered horizons.
In ANFIS structure e.g., structure (gauss 24100) for forecasting 1 week ahead of poultry retail price the terms gauss2, 4 and 100 represent the type of membership function, the number of membership function and the number of training epochs, respectively.
In addition, in ARIMA structure e.g., structure (113) for forecasting 1 week ahead of poultry retail price the terms 1, 1 and 3 represent the degree of auto regressive (p), the degree of integration (d) and the degree of moving average (q), respectively.
Table 2 states that ANFIS model provide the better forecasting results for Iran’s poultry retail price forecasting by all three performance measures.
Comparison of Linear and Nonlinear Models Performance to Poultry Retail
Price Forecasting
In order to compare the performance of considered linear and nonlinear models
to Iran’s poultry retail price forecasting, we divided the quantity of
forecast evaluation criterions of ANFIS to ARIMA model, per each horizon. Table
3 shows the results of these comparisons.
Table 2: 
ANFIS and ARIMA models performance to poultry retail price
forecasting 


Fig. 3: 
Sample fitness of ARIMA and ANFIS model, (a) 1 weak ahead,
(b) 2 weak ahead and (c) 4 weak ahead 
Table 3: 
Comparison the performance of various ANFIS and ARIMA structures 

According to Table 3, the ANFIS nonlinear model forecasting performance is better in contrast with the ARIMA linear model because (1) the RMSE, MSE and MAD divided are less than 1 and (2) the R^{2} divided is more than 1.
CONCLUSION
In this study, the accuracy of ANFIS as the nonlinear model was compared with ARIMA as the linear model for forecasting 1, 2 and 4 weeks ahead of weekly Iran’s poultry retail price. Results were indicated that ANFIS nonlinear model forecasts are considerably more accurate than the linear traditional ARIMA model which used as benchmarks in terms of error measures, such as RMSE and MAD. On the other hand, as the R^{2} criterion is concerned; ANFIS nonlinear model is absolutely better than ARIMA linear model. Briefly using forecast evaluation criteria we found that ANFIS nonlinear model outperforms ARIMA linear model. And we cannot deny that the ANFIS model is an effective way to improve the Iran’s poultry retail price forecasting accuracy.