Employs dm.hdf
over time to estimate the arrival of known specifications.
1 2  target.arrival.hdf(xdata, ydata, date, t, rts="crs",
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 
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.hdf
Distance measure using HDF
roc.hdf
RoC calculation using HDF
map.soa.hdf
SOA mapping using HDF
target.arrival.hdf
Arrival target setting using HDF
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # Estimate arrivals of MY2015 SC/TC 8 cylinder engines
# Load engine dataset
df < dataset.engine.2015
# Subset for SC/TC 8 cylinder engines
stc.8 < subset(df, grepl("^.C..", df[, 8]) & df[, 3] == 8)
# Parameters
x < subset(stc.8, select = 4)
y < subset(stc.8, select = 5:7)
d < subset(stc.8, select = 2)
# Generate an SOA map
target.arrival.hdf(x, y, d, 2014, "vrs")

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