State space models represent dynamic systems using hidden states and observed outputs, enabling predictions over time. Engineers apply them in control systems and signal processing, while economists use them for time series analysis. Data scientists benefit from their ability to handle noisy data, making them valuable in robotics, finance, and machine learning for robust forecasting.
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