demand model development for the retail sector of industry

Demand model development

for the retail sector of industry

Alexander Efremov

1/17 TU – Sofia, November 2014

Data Science Society

2005 – 2007 Consulting /Modelling & Strategy Optimization/ Auto-modelling Methodologies: Non-recursive & Recursive Fast Procedure for Cross-effects Determination



• Why demand models?

• Retail market specifics

• Market models

• Data preprocessing stage

• Modelling stage

• Validation

• Conclusions

Agenda



Agenda




• Market models


• Modelling stage

• Validation

• Conclusions


Why Demand Models?

• Demand Forecast

• Storage Control & Planning

• Strategy Optimization

• Detect & Explain Products Relations, etc.

4/17

?

? ?

TU – Sofia, November 2014


Agenda

5/17



• Market models


• Modelling stage

• Validation

• Conclusions



• Many inputs & outputs

─ Huge dimension:

single hypermarket has

107 inputs

105 outputs

─ Cross-related products

• Dynamic behavior

• Time-varying behavior

• Non-linear i/o behavior

• Inappropriate data sets

for modelling

Retail Market Specifics

6/17

MIMOsystem

u1,k

u2,k

um,k

y1,k

y2,k

yr,k



Agenda

7/17



• Market models


• Modelling stage

• Validation

• Conclusions



• Static Models, Time-Series

• Dynamic MIMO Models

Model types: ARX, ARMAX, ARARX, ARARMAX, BJ, OE

• Linear/Non-linear w.r.t. Parameters

• General Forms of MIMO Models

Parameter matrix form Model output = Parameters x Regressors

Parameter vector form Model output = Regressors x Parameters

Market Models



Agenda

9/17



• Market models


• Modelling stage

• Validation

• Conclusions



Data Preprocessing Stage

• Sample Window Analysis

• System Excitation Analysis

• Multicollienarity

• Missings – Interpolation, Quick and Dirty Models, etc.

• Outliers – Shaving

• Weights Proportional to the Number of Missings & Outliers

• Signals Decomposition (linear trend, seasonality, high freq.)

• Transformation

• Standardization



Agenda

11/17



• Market models


• Modelling stage

• Validation

• Conclusions



Modelling stage

• System Decomposition

─ Business knowledge: MIMO sub-systems

• Parameters Estimation

─ Weight factor accounting for

Missings & Outliers

─ Time-varying behavior

─ Numerically stable estimators

─ LS, CLS, ELS, GLS, EMLS,

PE-ARMAX, PE-ARARX, etc.


MIMOsystem

u1,k

u2,k

um,k

y1,k

y2,k

yr,k


Modelling stage

• Structure determination with SWR


y

pool P o

f fa

ctors

out

of

the m

odel

set

S o

f fa

ctors

in t

he m

odel Forward

selection

Backwardelimination

sj - most non sign.

u, y

pi - most sign.

S

SLE

SLS

P

y


Agenda

14/17



• Market models


• Modelling stage

• Validation

• Conclusions



Validation stage

• Validation

─ Development & validation data sets

─ Overall model measures: R2_adj, AIC, BIC, etc.

─ Partial measures: Standard error, Partial F, etc.

─ Final model development with overall data set



Agenda

16/17



• Market models


• Modelling stage

• Validation

• Conclusions



Conclusions

• Auto-modelling

─ Applicable for experimental modelling

─ The only way to develop hypermarket demand models

─ Scalable approach

• Accuracy

─ Dynamic models

─ Weight factor depending on the number of Missings & Outliers

─ Forgetting factor





Thank You!

Questions?

Alexander Efremov [email protected]



Laboratory

of Modelling

- 25 places

- Hadoop cluster

- Internships

- Courses

- Meeting between

students & business

demand model development for the retail sector of industry

Data & Analytics

demand models

dirty models

static models

demand model development

mimo system u1

crosseffects determination

inputs outputs huge

products relations