Research on Dynamic Market Demand Forecasting based on Machine Learning

Xiang Yue ()
Additional contact information
Xiang Yue: London School of Economics and Political Science

A chapter in Proceedings of the 2024 6th International Conference on Economic Management and Model Engineering (ICEMME 2024), 2025, pp 289-296 from Springer

Abstract: Abstract In the contemporary market, which is characterised by rapid change, the ability to accurately forecast consumer demand is of paramount importance for businesses seeking to optimise a range of operational aspects, including inventory management, pricing strategies and supply chain operations. This work puts forth a dynamic market demand forecasting model based on the Long Short-Term Memory (LSTM) network. It employs the formidable time series modeling capacity of LSTM to discern long-term dependencies and intricate nonlinear relationships in market demand data. An adaptive training mechanism has been introduced, enabling the model to adjust its weights in real time based on new input data, thereby improving its responsiveness to market fluctuations. Furthermore, the proposed model enhances prediction accuracy and mitigates the uncertainty associated with manual parameter tuning by optimizing the automatic search mechanism for hyperparameters. The LSTM model is capable of processing multidimensional input data, including historical sales data, promotional activities, seasonal factors, and external economic indicators, in order to comprehensively understand the key factors influencing market demand. The experimental results demonstrate that the proposed model exhibits notable advantages in capturing the intricate dynamic alterations of market demand, particularly in the domain of forecasting long-term series and multidimensional influencing factors. Its predictive precision is superior to that of alternative models.

Keywords: Dynamic market demand forecasting; Long short-term memory networks; Time series modeling; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-690-1_28

Ordering information: This item can be ordered from
http://www.springer.com/9789464636901

DOI: 10.2991/978-94-6463-690-1_28

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().