Retail Demand Forecasting: A Comparative Analysis of Deep Neural Networks and the Proposal of LSTMixer, a Linear Model Extension
Accurate retail demand forecasting is integral to the operational efficiency of any retail business. As demand is described over time, the prediction of demand is a time-series forecasting problem which may be addressed in a univariate manner, via statistical methods and simplistic machine learning...
Saved in:
| Main Authors: | Georgios Theodoridis, Athanasios Tsadiras |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/7/596 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Forecast of sugar demand in retail using SARIMA and decomposition models case study: a retail store in Indonesia
by: Titi Sari, et al.
Published: (2025-04-01) -
Resource Time Series Analysis and Forecasting in Large-Scale Virtual Clusters
by: Yue Lin, et al.
Published: (2025-05-01) -
Forecasting Model Selection of Curly Red Chili Price at Retail Level
by: Ketut Sukiyono, et al.
Published: (2019-03-01) -
Forecasting Model Selection of Curly Red Chili Price at Retail Level
by: Ketut Sukiyono, et al.
Published: (2019-03-01) -
Retail Sales Forecasting Using Deep Learning: Systematic Literature Review
by: Linda Eglite, et al.
Published: (2022-04-01)