A SHORT-TERM FORECASTING MODEL WITH INHIBITING NORMAL DISTRIBUTION NOISE OF SALE SERIES

Figures & data

FIGURE 1 Forecasting method based on ECPSO and g-SVM. (Color figure available online.)

TABLE 1 Influencing Factors of Product Sale Forecasting

Product characteristics	Unit	Expression
Brand famous degree (BF)	Dimensionless	Linguistic information
Performance parameter (PP)	Dimensionless	Linguistic information
Form beauty (FB)	Dimensionless	Linguistic information
Sales experience (SE)	Dimensionless	Linguistic information
Oil price (OP)	Dimensionless	Linguistic information
Dweller deposit (DD)	Dimensionless	Linguistic information

TABLE 2 Performance of PSO and ECPSO

Algorithm	Iterative number	MSE
		Maximum	Minimum
PSO	8	0.000811549	0.000345837
ECPSO	7(Child process)	0.00071749	0.000378025

FIGURE 2 Forecasting results from ECPSOg-SVM model. (Color figure available online.)

TABLE 3 Forecasting Results from Four Models

No.	Original data	Forecasting value
		ARMA	PSO v-SVM	ECPSO v-SVM	ECPSO g-SVM
1	1508	1656	1527	1520	1506
2	1808	1654	1819	1815	1805
3	1775	1624	1776	1782	1780
4	2382	1642	2329	2356	2378
5	1703	1656	1699	1713	1706
6	1383	1631	1385	1387	1388
7	2042	1656	1991	2010	2025
8	1235	1672	1287	1256	1245
9	1139	1731	1139	1148	1143
10	627	1665	704	685	644
11	1921	1679	1938	1931	1926
12	1589	1641	1605	1598	1591

TABLE 4 Error Analysis on Four Different Models

Model	MAE	MAPE	MSE
ARMA	352.92	0.3052	209070
PSO v-SVM	106.75	0.0663	82758
ECPSO v-SVM	17.08	0.0150	510
ECPSO g-SVM	6.42	0.0053	67.58