Hidden Markov Models for Time Series - Walter Zucchini, Iain L. MacDonald, Roland Langrock - książka wyd. 2021

Opis

Published by Chapman & Hall/CRC in 2016 (second edition), the book is written for statisticians, data scientists, and researchers across fields such as finance, biology, engineering, and environmental science who want to understand and apply HMMs in practice.

The authors begin with the foundations of hidden Markov models, explaining the theory behind state-space models, stochastic processes, and the probabilistic structure of HMMs. They then move into practical applications, showing how HMMs can be used to model complex time series with underlying regimes or states that are not directly observable. The book emphasizes implementation in R, providing code examples, algorithms, and exercises that allow readers to apply the methods to real data.