Team Name: Outliers This the final project presentation for AIT 582 course of George Mason University. Demand forecasting plays is vital in today's world for the retail business. Proper demand management of store items directly impacts the efficiency of running a retail business. As the retail stores are in direct contact with the clients (shoppers) demands on a daily basis, demand forecasting is a key driver for such businesses. The goal of the project is to identify the best analytical method to solve such problems. The project data consist of 5 years of store-item sales data, and we plan to predict 3 months of sales for 50 different items at 10 different stores. We plan to see how we can deal with the seasonality of the demand for different items? Is forecasting demand for the items for individual stores more efficient or should the analysis should of the combined stores altogether. We plan to see which methods are optimal for such kind of analysis.

1. Store Item Demand
Forecasting
--
AIT 582: Applications of Metadata in Complex Big Data Problems
RAHUL PANDEY
ASHUTOSH DEOGHARE
UTSAV GAIKWAD

2. Outline
Introduction
Research Problem
Related Work
Research Method
◦ Data
◦Exploration and Visualization
◦Preprocessing
◦ Experimental Method
◦S-Naïve
◦S-ARIMA
◦Neural Network
◦ Evaluation
◦Evaluation Metric
◦Analysis and Visualization
Conclusion(s) & Future Work

3. Introduction
1. INTRODUCTION
2. RESEARCH PROBLEM

4. Introduction
Demand Forecasting is the process in which historical sales data is used to develop an
estimate of an expected forecast of customer demand.
 Advantages
i. Company/Product
 Better Product lifecycle, inventory optimization, expanding business
ii. Suppliers
 Budget Preparation, Performance Management
iii. Customers
 Increased Customer Satisfaction, reduced inventory stockouts
Source: https://intrigosys.com/planning/processes/supply-and-demand-
planning/

5. Research
Problem
GIVEN THE SALES OF DIFFERENT ITEMS IN DIFFERENT
STORE IN A TIME SERIES, CAN WE PREDICT THE SALES OF
NEXT QUARTER FOR EACH ITEM IN EACH STORE?

6. Related Work

7. Related Work
Supply vs Demand
◦ (Juster, 2006; Silva et al., 2008)
Moving/Average + Planning Committee
o Gives better Accuracy
o Sometimes biased results
o Eradicating biases (Fildes et al., 2009)
Fuzzy logic time series vs Classical time series
o (Song and Chissom, 1993)
Adding Non-linearity
o ANN (Silva et al., 2008; Fildes et al., 2009)
Accuracy
5%🔝
Order
Perfection
10%🔝
(Hasin, 2008)

8. Research Method
1. DATA
2. EXPERIMENTAL METHOD
3. EVALUATION

9. Research Methodology

10. Data
Number of
Instances:
1826*10*50
=913000
Duration: Jan 1,
2013 – Dec 31,
2017
10 stores | 50
items
Training Number of
Instances:
90*10*50
=45000
Duration: Jan 1,
2018– Mar 31,
2018
10 stores | 50
items
Testing
https://www.kaggle.com/c/demand-forecasting-kernels-only/data

11. Data: Exploration and Visualization

12. Data: Exploration and Visualization
Shopping in
Summers?

13. Data: Exploration and Visualization
Shopping in
Weekends?

14. Data: Exploration and Visualization

15. Data: Preprocessing
Number of
Instances:
1005*3*3
Duration: Jan
1, 2015– Oct
2, 2017
3 stores | 3
items
Training
Number of
Instances:
90*3*3
Duration: Oct
3, 2017– Dec
31, 2017
3 stores| 3
items
Testing
https://www.kaggle.com/c/demand-forecasting-kernels-only/data
 Because of
computation
power, we have
used 2 years 9
months of data for
training and next 3
month (1 quarter)
for prediction
 We have analyzed 3
stores and 3 items
for our proposed
approach

16. Data: Exploration and Visualization
Seasonality
in data?

17. Experimental
Method
BASELINE (S-NAÏVE)
NEURAL NET
S-ARIMA

18. Experimental Method: S-Naïve
 Seasonal Naïve Model
 Naïve Model: Forecast for any period equals the previous period’s actual value
 where k is the seasonality lag
 Works well with highly seasonal data

19. Experimental Technique/Modelling
Model 1: Seasonal ARIMA (SARIMA)
An extension to ARIMA that supports the seasonal component of the time series.
Regular AR, I, MA component Seasonal component

20. Experimental Technique/Modelling
Model 2: Neural Net (NNETAR)
The nnetar function in the forecast package for R.
Feed-forward neural network with a single hidden layer.
Uses lagged inputs for forecasting univariate time series.
Autoregressive Model.
Checks seasonality.

21. Evaluation
1. EVALUATION METRIC
2. ANALYSIS AND VISUALIZATION

22. Evaluation Metric
Evaluation Metric: SMAPE
Symmetric Mean Absolute Percent Error
Target: Reduce SMAPE error across the test data

23. Evaluation: Analysis and Visualization

24. Evaluation: Analysis and Visualization

25. Evaluation: Analysis and Visualization

26. Evaluation: Analysis and Visualization
Overall Predictions for Item 1

27. Conclusion and Future Work
Conclusion
The project is focused on predicting sales of an item given their sales from previous years
After exploratory data analysis we found the data to have seasonal patterns which led us to use seasonal time
series models
Our proposed two approaches(S-ARIMA and NN) for predicting the sales of the next 90 days were compared
with the baseline approach i.e. Seasonal Naïve Model
The analysis concluded that using Seasonal ARIMA outperforms the baseline and Neural Network with low
SMAPE
The Neural Network approach performed worse than baseline because of data being univariate
Future Work
Analyzing other stores and items to get the generalizability of the performance across the models
We can also use feature engineering to extract day, month, and year from date and solve the problem using
regression

28. Thank you
QUESTIONS?