Employs dm.sf
over time to estimate the arrival of known specifications. This function is valid only when multiplicative form of directional vector is used.
1 2  target.arrival.sf(xdata, ydata, date, t, rts="crs", g=NULL,
wd=NULL, sg="ssm", ftype="d", cv="convex")

xdata 
Input(s) vector (n by m) 
ydata 
Output(s) vector (n by s) 
date 
Production date (n by 1) 
t 
A vantage point from which the RoC is captured 
rts 
Returns to scale assumption 
g 
Directional vector indicating a measurement direction (n by (m+s)) 
wd 
Weak disposability vector indicating (an) undesirable output(s) (1 by s) 
sg 
Employs secondstage optimization 
ftype 
Frontier type 
cv 
Convexity assumption 

Efficiency at 

Intensity vector at 

Effective date 

Average RoC 

Local RoC 

Individualized RoC 

Estimated arrival using 

Estimated arrival using 
DongJoon Lim, PhD
Lim, DongJoon, et al. "Comparing technological advancement of hybrid electric vehicles (HEV) in different market segments." Technological Forecasting and Social Change 97 (2015): 140~153.
Lim, DongJoon, and Timothy R. Anderson. Time series benchmarking analysis for new product scheduling: who are the competitors and how fast are they moving forward?. Advances in DEA Theory and Applications: with Examples in Forecasting Models. Wiley (forthcoming), 2016.
dm.sf
Distance measure using SF
roc.sf
RoC calculation using SF
map.soa.sf
SOA mapping using SF
target.arrival.sf
Arrival target setting using SF
1 2 3 4 5 6 7 8 9 10 11 12  # Estimate arrivals of MY2013 hev models
# Load hev dataset
df < dataset.hev.2013
# ready
x < subset(df, select = 3)
y < subset(df, select = 4 : 6)
d < subset(df, select = 2)
g < data.frame(x, y)
# go
target.arrival.sf(x, y, d, 2012, "vrs", g)$arrival_seg

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