Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model

Table 1

Results of an exploratory study.

S. No.	References	No. of datasets			Model accuracy (MAPE in %)		Running time (seconds of CPU time)
		Total	Positive	GBM	LR	ARIMA	LR	ARIMA
1	Box et al. [24]	20	16	13	0.42–9.17	0.42–8.21	0.08–0.26	4.80–5.96
2	Brockwell and Davis [29]	40	27	20	0.26–9.84	0.25–9.36	0.04–0.22	5.10–6.44
3	Cryer and Chan [30]	48	35	24	0.14–8.93	0.14–7.60	0.06–0.28	4.85–6.11
4	Harvey [31]	18	17	8	0.29–8.93	0.31–6.76	0.04–0.11	4.84–5.75
5	Montgomery et al. [32]	25	24	20	0.03–7.69	0.05–7.36	0.04–0.31	5.00–6.28
	Total	151	119	85

Note: number of datasets with positive data; number of positive datasets explored using GBM law.