def model(self,beta):		

		Y = np.array(self.data[self.max_lag:len(self.data)])
		X = np.ones(len(Y))

		# Transform parameters
		parm = [self.param_desc[k]['prior'].transform(beta[k]) for k in range(len(beta))]

		# AR terms
		if self.ar != 0:
			for i in range(self.ar):
				X = np.vstack((X,self.data[(self.max_lag-i-1):(len(self.data)-i-1)]))

		mu = np.matmul(np.transpose(X),parm[0:len(parm)-1-self.ma])

		# MA terms
		if self.ma != 0:
			for t in range(self.max_lag,len(Y)):
				for k in range(self.ma):
						mu[t] += parm[1+self.ar+k]*(Y[t-1-k]-mu[t-1-k])

		return mu, Y
@since: 0.5
dataset/ETHPy150Open RJT1990/pyflux/pyflux/arma/arma.py/ARIMA.model--crossover with--dataset/ETHPy150Open AppScale/appscale/AppServer/lib/PyAMF-0.6.1/pyamf/python.py/isNegInf