Counterfactual Demand Forecasting Using Multivariate LSTM

Title

Counterfactual Demand Forecasting Using Multivariate LSTM

Creator

Khatija, Liya; Vinoth, Dalvin; Kavitha, R.

Description

Demand forecasting is a key part of running operations efficiently in the fast-changing retail and online shopping industries. Regular methods that use statistics often have trouble handling the complex, changing, and time-based patterns found in actual sales data. This study introduces a new way to predict demand that uses multivariate Long Short-Term Memory (LSTM) models. The models take both the order of sales over time and other factors like prices and weather into account. Three model designs were tested: a simple straightforward model, a pure LSTM model, and a new hybrid LSTM model that mixes time-based data with steady economic factors. The combined hybrid model worked the best, by successfully balancing learning from sequences with keeping things stable. The study did experiments to see what would happen if weather conditions changed, like extreme heat, cold, storms, or dry spells and compared normal forecasts with these changed scenarios to see how demand would shift for products and overall sales. The results show that this new framework not only makes better predictions but also gives useful information on how weather events can affect store sales. By linking prediction with 'what if' analysis, this research moves demand forecasting from just predicting what will happen to helping make better decisions. 2025 IEEE.

Source

Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN;Issue;2025;pp.217-221

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

counterfactual analysis; Demand forecasting; multivariate LSTM; retail time series; weather-aware forecasting

Coverage

Khatija L., Christ (Deemed-to-be University), Bengaluru, India; Vinoth D., Christ (Deemed-to-be University), Bengaluru, India; Kavitha R., Christ (Deemed-to-be University), Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 28323645;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICRCICN68210.2025.11365012

https://www.scopus.com/pages/publications/105035365805?origin=resultslist