Time Series Trends Forecasting for Manufacturing Enterprises in the Digital Age

Abstract

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.