obtain inter-arrival from bulk arrival
I have some data regarding the arrival of customers at a shop. These arrivals are bulk arrivals which have been collected at fixed time intervals (say, t1=0+x,t2=t1+x,t3=t2+x,..).
How can I convert these bulk arrivals to inter-arrivals?
what about using uniform distribution for this purpose and distribute the number of customer between t and t+x and then censor anything less than x as I shown below?
More explanation as requested:
Data look likes this
Please assume x is 3600 seconds, as data have been collected hourly.
start, t0
first hour, t1=t0+x, 10 customers
second hour, t2=t1+x=t0+2x, 20 customers
third hour, t3=t2+x=t0+3x, 50 customers
...
...
..
23 hour, t23=t22+x=t0+23x, 4 customers
So, the above shows the arrival times.
and I would like to know the which distribution inter-arrival rate of customers has. In simple words, I would like to know after how many seconds I shall expect a customer in the shop and what distribution can model this inter-arrival.
What do you think of this approach:
Please let me know if you need more information.
Thanks
I have some data regarding the arrival of customers at a shop. These arrivals are bulk arrivals which have been collected at fixed time intervals (say, t1=0+x,t2=t1+x,t3=t2+x,..).
How can I convert these bulk arrivals to inter-arrivals?
what about using uniform distribution for this purpose and distribute the number of customer between t and t+x and then censor anything less than x as I shown below?
More explanation as requested:
Data look likes this
Please assume x is 3600 seconds, as data have been collected hourly.
start, t0
first hour, t1=t0+x, 10 customers
second hour, t2=t1+x=t0+2x, 20 customers
third hour, t3=t2+x=t0+3x, 50 customers
...
...
..
23 hour, t23=t22+x=t0+23x, 4 customers
So, the above shows the arrival times.
and I would like to know the which distribution inter-arrival rate of customers has. In simple words, I would like to know after how many seconds I shall expect a customer in the shop and what distribution can model this inter-arrival.
What do you think of this approach:
Code:
for each day
{
sl=a null list
do this for all t_i(s)
{
ss=runiform(number of observed customers at t_i, min=t_i-1,max=t_i)
ss=sort(ss)
sl=c(sl,ss)
}
to obtain the inter-arrivals :
lis= a null list
for i in 2:length(sl)
{
lis=c(lis, abs(sl[i]-sl[i-1]))
}
left censor anything less than x, and x is (t_i)-(t_i-1), which is 3600 sec
and then fit,
}
Please let me know if you need more information.
Thanks
Last edited: