Time Series Trends Forecasting for Manufacturing Enterprises in the Digital Age

In the digital age, manufacturing enterprises face challenges like information overload and data fragmentation. To address these issues, this paper proposes a novel method that integrates the Improved Whale Optimization Algorithm (IWOA), Bidirectional Long Short-Term Memory (BILSTM), and Temporal Pa...

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Bibliographic Details
Published in:Journal of organizational and end user computing 2024-06, Vol.36 (1), p.1-22
Main Authors: Yang, Chaolin, Yan, Jingdong, Wang, Guangming
Format: Article
Language:English
Subjects:
Algorithms
Digital economy
Industrial development
Manufacturing
Optimization algorithms
Pattern analysis
Production planning
Supply chain management
Supply chains
Time series
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Summary:In the digital age, manufacturing enterprises face challenges like information overload and data fragmentation. To address these issues, this paper proposes a novel method that integrates the Improved Whale Optimization Algorithm (IWOA), Bidirectional Long Short-Term Memory (BILSTM), and Temporal Pattern Attention (TPA) for analyzing time series data. IWOA optimizes hyperparameters, BILSTM captures temporal dependencies, and TPA enhances interpretability. Experimental results show the method's effectiveness in market trend prediction, production planning, and supply chain management. It enables accurate forecasts in a competitive environment, enhancing flexibility and foresight. This research overcomes existing limitations, offering a valuable analytical tool for understanding the digital economy's impact on manufacturing enterprises. It provides guidance for the industry's development in the digital era.
ISSN:1546-2234
1546-5012
DOI:10.4018/JOEUC.345242