Statistical Methods for Predicting Malaria Incidences Using Data from Sudan

Table 4

Models, parameter estimates, and fit statistics for malaria incidence: Khartoum State.

Model	Variable	Coefficient	Std. error	-statistic	Prob.		AIC	MAE
ARIMA		404.53	23.61	17.08	<0.001	0.86	12.15	153.65
	AR(1)	1.92	0.16	11.80	<0.001
	AR(2)	−1.28	0.29	−4.36	<0.001
	AR(3)	0.31	0.16	1.92	0.063
	MA(1)	−0.99	0.12	−7.96	<0.001
	MA(2)	−0.94	0.03	−28.42	<0.001
	MA(3)	0.93	0.12	7.64	<0.001
Exponential smoothing	AR(4)	0.49	0.13	3.81	0.001	0.26	12.08	116.09
	MA(4)	−0.94	0.04	−24.03	<0.001
Transformation				−0.73	0.471	0.07	−13.30	000
	AR(1)	−0.29	2.42	−0.12	0.906
	AR(2)	0.17	0.80	0.21	0.839
	MA(1)	0.56	2.43	0.23	0.820
	MA(2)	−0.11	1.33	−0.09	0.937
Moving average	AR(1)	0.28	0.12	2.26	0.028	0.12	11.56	53.15
	AR(2)	0.53	0.21	2.65	0.011
	MA(1)	−0.21	0.00	−300.98	<0.001
	MA(2)	−0.77	0.10	−7.49	<0.001