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A Hybrid Deep Learning Framework for Enhanced Time Series Forecasting in Complex Industrial Processes

Authors: Pankaj Pachauri

Published: 2025-05-02 10:19:35.174

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Abstract

Accurate time series forecasting is crucial for optimizing complex industrial processes, enabling proactive decision-making and minimizing operational costs. Traditional statistical methods often struggle to capture the intricate non-linear dependencies and long-range dependencies inherent in such processes. This paper proposes a novel hybrid deep learning framework that combines the strengths of Long Short-Term Memory (LSTM) networks and Transformer architectures to enhance time series forecasting accuracy in complex industrial settings. The framework leverages LSTM networks for capturing local temporal patterns and Transformer networks for modeling long-range dependencies and contextual information. Furthermore, we incorporate a feature engineering module to extract relevant features from raw sensor data, improving the model's ability to learn complex relationships. We evaluate the proposed framework on a real-world industrial dataset and demonstrate its superior performance compared to state-of-the-art time series forecasting models. The results highlight the effectiveness of the hybrid approach in capturing both short-term and long-term dependencies, leading to significant improvements in forecasting accuracy and enabling more effective process optimization.

Keywords

Time Series Forecasting, Deep Learning, Hybrid Models, LSTM, Transformer, Industrial Processes, Anomaly Detection, Feature Engineering, Sequence-to-Sequence, Attention Mechanism

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How to Cite

Pankaj Pachauri, (2025-05-02 10:19:35.174). A Hybrid Deep Learning Framework for Enhanced Time Series Forecasting in Complex Industrial Processes. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 3.

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