Hybrid Deep Learning and Evolutionary Algorithms for Multivariate Time Series Forecasting in Industrial Applications

The increasing complexity and dynamic nature of industrial systems have elevated the importance
of accurate multivariate time series forecasting for critical functions such as predictive maintenance,
anomaly detection, process optimization, and operational planning. Traditional deep learning
approaches, while powerful in capturing non-linear temporal dependencies, face significant limitations
in real-time environments due to their computational demands, sensitivity to hyperparameter
configurations, and lack of adaptability. To address these challenges, this chapter presents a
comprehensive study on hybrid deep learning and evolutionary algorithm frameworks, designed to
enhance forecasting accuracy, robustness, and real-time adaptability in industrial applications. The
proposed hybrid architectures leverage the temporal modeling strengths of deep networks and the
global optimization capabilities of evolutionary algorithms to automate hyperparameter tuning,
improve generalization, and adapt to concept drift in streaming data. Comparative evaluations with
non-hybrid models highlight the superior performance of hybrid methods in terms of accuracy, fault
tolerance, and computational efficiency. Emphasis was also placed on the practical deployment of
these models in live industrial settings, focusing on their ability to maintain reliability under data noise,
sensor faults, and dynamic operational conditions. This chapter contributes to the advancement of
intelligent, resilient, and scalable forecasting systems tailored for Industry 4.0 environments.
Keywords: Multivariate Time Series, Deep Learning, Evolutionary Algorithms, Real-Time
Forecasting, Fault Tolerance, Industrial AI