Abstract

Accurate time series forecasting is crucial for optimizing operations and decision-making in dynamic industrial environments. This paper proposes a novel hybrid deep learning framework that integrates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) architectures to capture both temporal dependencies and local patterns within time series data. The framework is designed to adapt to the non-stationary nature of industrial processes, incorporating mechanisms for anomaly detection and robust performance in the presence of noise and outliers. We evaluate the performance of the proposed framework on real-world industrial datasets, demonstrating its superior accuracy and robustness compared to traditional time series forecasting methods and individual deep learning models. Furthermore, we analyze the impact of different hyperparameters and architectural configurations on the forecasting performance, providing insights into the optimal design of hybrid deep learning models for industrial time series data. The results highlight the potential of the proposed framework for predictive maintenance, resource optimization, and improved operational efficiency in dynamic industrial settings.