Abstract
Daily retail sales are impacted by a lot of external
factors, holidays and special events are one such category. In
general, retail sales are largely impacted by fluctuations in
demand hence, it is common for a retailer to run out of stock for
some items and overstock the other items and this happens due
to the lack of understanding of the actual number of items in
demand for a particular item at a particular time of the month.
This research work examines the impact of holidays or special
events on the sales of a wide category of items using predictive
analytics. It is done by performing exploratory data analysis and
pre-processing methods followed by feature engineering and
information extraction to extract the optimal input parameters
to be fed into the model. The dataset is time-series data however,
advanced machine learning algorithms are also used along with
time-series methods to see if time-series data works well with
non-time-series algorithms. Different time-series methods along
with gradient boosting and the Facebook prophet model are
evaluated in this work, achieving 92.83% forecast accuracy with
the Facebook prophet model. The gradient boosting model
performs well with a MAPE value of 22.25% and time-series
Holt Winters' additive method provides a MAPE value of
12.84%. Each of the algorithms provides a good score with this
time-series data and an appropriate algorithm can be chosen as
per the business need.
factors, holidays and special events are one such category. In
general, retail sales are largely impacted by fluctuations in
demand hence, it is common for a retailer to run out of stock for
some items and overstock the other items and this happens due
to the lack of understanding of the actual number of items in
demand for a particular item at a particular time of the month.
This research work examines the impact of holidays or special
events on the sales of a wide category of items using predictive
analytics. It is done by performing exploratory data analysis and
pre-processing methods followed by feature engineering and
information extraction to extract the optimal input parameters
to be fed into the model. The dataset is time-series data however,
advanced machine learning algorithms are also used along with
time-series methods to see if time-series data works well with
non-time-series algorithms. Different time-series methods along
with gradient boosting and the Facebook prophet model are
evaluated in this work, achieving 92.83% forecast accuracy with
the Facebook prophet model. The gradient boosting model
performs well with a MAPE value of 22.25% and time-series
Holt Winters' additive method provides a MAPE value of
12.84%. Each of the algorithms provides a good score with this
time-series data and an appropriate algorithm can be chosen as
per the business need.
| Original language | English |
|---|---|
| Title of host publication | 2023 15th International Conference on Developments in eSystems Engineering (DeSE) |
| Editors | Dhiya Al-Jumelly, Header Abed Dhahad, Manoj Jayabalan, Jade Hind, Jamila Mustafina, Sulaf Assi, Abir Hussain, Hissam Tawfik |
| Publisher | IEEE |
| Pages | 131-136 |
| ISBN (Electronic) | 9798350335149 |
| ISBN (Print) | 9798350335156 |
| DOIs | |
| Publication status | Published - 17 Apr 2023 |
| Externally published | Yes |
| Event | 2023 15th International Conference on Developments in eSystems Engineering - Baghdad, Iraq Duration: 9 Jan 2023 → 12 Jan 2023 Conference number: 15 |
Conference
| Conference | 2023 15th International Conference on Developments in eSystems Engineering |
|---|---|
| Abbreviated title | DESE |
| Country/Territory | Iraq |
| City | Baghdad |
| Period | 9/01/23 → 12/01/23 |