blind test

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

reference vs reference

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 reference vs reference + gaussian noise, a single location might have low correlation

reference reference vs reference + gaussian noise, overall sum neutralizes the gaussian noise

scor1 score gauss noise 50%

scor1 score gauss noise 90% no smoothing, final score on 30 days

scor_ref score gauss noise 30%, final score on june

scor_ref score gauss noise 20%, final score on june

day correlation mapping

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

cilac sum no day filtering

We have to filter out bad days

cilac sum bad day filtering

We perform a weekday correction

cilac sum weekday correction

cilac_cor 2d correlation between cilac patterns

country adjustment country adjustment

etl - lowcount - wday - filter - reg 00 - 04 - 22 - 30 - 41

scor new mapping iteration of the new mapping scoring

blind test on real data

play equal_learn test on performance on learn & play on same days

learn_play smoothing correlates neighboring events and improves the score, june in blind test

curve blind curve blind, june

curve blind_single2 curve blind_single2

curve blind_single curve blind_single

learn_play scoring on the different learning steps until blind test

learn play_randomDays learn play_randomDays

correlation over location correlation over locations