Prophet with Exogenous Variables for Procurement Demand Prediction under Market Volatility

null null

doi:10.70393/6a6374616d.333237

OPEN ACCESS |Research Article ||4 November 2025

Prophet with Exogenous Variables for Procurement Demand Prediction under Market Volatility

null null

Duke University

sichong.huang@alumni.duke.edu

Duke University, USA.

* Corresponding Author¹: null null, E-Mail: sichong.huang@alumni.duke.edu

Publication

Accepted 2025 October 18 ; Published 2025 November 4

Journal of Computer Technology and Applied Mathematics, 2025, 2(6), 3007-4126.

Abstract

Addressing the issue of insufficient accuracy in procurement demand forecasting under market volatility, this study investigates the Prophet model with exogenous variables. It outlines the comprehensive workflow encompassing data preprocessing, feature reconstruction, and model training, while introducing trend decomposition and forecasting implementation methods constrained by multi-source features. Comparative experimental results demonstrate that the improved model reduces RMSE by 21.5% and MAPE by 34.2% in high-volatility intervals, significantly enhancing prediction stability. This validates the effective corrective role of exogenous variables in addressing complex market disturbances.

Keywords

Prophet Model , Exogenous Variables , Market Volatility , Procurement Demand Forecasting .

Metadata

DOI:

10.70393/6a6374616d.333237

ARK:

ark:/40704/JCTAM.v2n6a03

Pages: 15-20

References: 5

Disciplines: Big Data Technology

Subjects: Data Analytics

Cite This Article

APA Style

Unknown Author (2025). Prophet with exogenous variables for procurement demand prediction under market volatility. Journal of Computer Technology and Applied Mathematics, 2(6), 15-20. https://doi.org/10.70393/6a6374616d.333237

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Kang, M. (2025). Research on prediction model and optimization of enterprise material procurement management based on global linkage. International Journal of Computational Intelligence Systems, 18(1), 242.

2.

Kao, C., Liu, L., & Sun, R. (2025). A bias-corrected fixed effects estimator for the dynamic panel data model with exogenous variables. Economics Letters, 254, 112426.

3.

Setiawan, S., Sohibien, D. P. G., Prastyo, D. D., et al. (2024). Addition of subset and dummy variables in the threshold spatial vector autoregressive with exogenous variables model to forecast inflation and money outflow. Economies, 12(12), 352.

4.

Sel, B., & Minner, S. (2025). Probabilistic forecast-based procurement in seaborne forward freight markets under demand and price uncertainty. Transportation Research Part E, 193, 103830.

5.

Huang, Z., & Ma, Z. (2024). Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model. Reliability Engineering and System Safety, 252, 110485.

PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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