What algorithm can I use to compute number of say positive or negative postings seen until a certain timepoint? - data-science

Question

I wish to check if my understanding and proposed algorithm below would be correct.
to calculate the number of positive postings I have seen until time point ti, I am proposing a loop as below:
sumofPi = 0
for x = 0 until x = ti
sumofPi = sumofPi + Pi-1
I am not sure if this will work but the idea is to be able to sum up the positive postings that comes in within a certain timepoint in a data stream.
Thanks

Answer 1

The sequence seems fine as long as the events are indexed in order and you are comfortable loosing events that happened at the same time but indexed differently as a result of that limitation. You may also want to address posting type filtering.
Your algorithm in Python:
# Sample data
postingevents=[1,0,1,1,0,1]
# Algorithm:
sumofPi = 0
ti=4
for i in range(0,ti):
sumofPi += postingevents[i]
print(sumofPi)
3
Looks like you are dealing with time series.
For time series, I would suggest rolling sum or rolling weighted averages, there's an example here
Below are some Python code samples using loops and recursion with a data sample (Event indicator & epoch time stamp)
# Data sample:
postingevents=[1,0,1,1,0,1]
postingti=[1497634668,1497634669,1497634697,1497634697,1497634714,1497634718]
postings=([postingevents,postingti])
# All events preceeding time stamp T. Events do not need to be ordered by time.
def sumpi_notordered(X,t):
return sum([xv if yv<=t else 0 for (xv,yv) in zip(X[0],X[1])])
# Sum ordered events indexed by T, using recursion.
def sumpi_ordered(X,t):
if t>=1:
return X[t]+sumpi_ordered(X,t-1)
else:
return(X[t])
print(sumpi_notordered(postings,1497634697))
3
print(sumpi_ordered(postingevents,3))
3