Hidden Markov Models for Time-Series Analysis

Hidden Markov Models are useful when a system changes over time but the true internal state cannot be directly observed. They provide a structured way to infer hidden states from observed measurements, making them valuable for localization, signal detection, and sequence modeling.

In engineering systems, HMMs can help model temporal dynamics in noisy data. For example, Wi-Fi signal measurements can be used to infer location states, while acoustic or sensor signals can be used to detect patterns over time.

Although deep learning is widely used today, probabilistic models such as HMMs remain valuable because they are interpretable, computationally efficient, and well suited for systems where temporal structure matters.