Our function can be expressed as:
xact(t)mshare(td) = yref(t)../../f/fpeople(t) + yout(t)
where xact is the number of activities, mshare is the market share, yref reference data - cachier receipts, ../../f/fpeople people per receipt, yout people outside the shop, t time, td day
To check the capability of the prediction we take as reference the customer data and as activities the customer data multiplied by a random noise
reference vs
reference + gaussian noise, a single location might have low
correlation
reference vs
reference + gaussian noise, overall sum neutralizes the gaussian
noise
score gauss noise 50%
score gauss noise 90%
no smoothing, final score on 30 days
score gauss noise
30%, final score on june
score gauss noise
20%, final score on june
We take the first 20 cilacs close to a poi and we calculate activites on daily basis.
Activities are processed with a 20km previous distance filter and we match activity chirality with the poi chirality.
6% of the total cilacs correlates over 0.6 with [reference data../../f/f_year.csv.gz).
The sum of the activities over all country is
no day filtering
We have to filter out bad days
bad day filtering
We perform a weekday correction
weekday correction
2d correlation between cilac patterns
country
adjustment
etl - lowcount - wday - filter - reg 00 - 04 - 22 - 30 - 41
iteration of
the new mapping scoring
test on
performance on learn & play on same days
smoothing
correlates neighboring events and improves the score, june in blind
test
curve blind, june
curve
blind_single2
curve
blind_single
scoring on the different learning steps until
blind test
learn
play_randomDays
correlation over locations