Time-Series Analysis for State Space Modeling with Stan: A Comprehensive Guide

Time-series analysis is a powerful statistical technique used to analyze and predict temporal data. It finds applications in various fields, including econometrics, finance, healthcare, and engineering. State space models (SSMs) are a class of time-series models that explicitly represent the underlying dynamic system that generates the data. They provide a flexible framework for modeling complex time series with unobserved components, such as trends, seasonality, and latent variables.

Stan is a probabilistic programming language specifically designed for Bayesian statistical modeling. It allows users to define complex statistical models and perform Bayesian inference efficiently. Stan uses Hamiltonian Monte Carlo (HMC) sampling, a powerful Markov chain Monte Carlo (MCMC) algorithm, to explore the posterior distribution of the model parameters.

Combining the power of SSMs and Stan provides a highly effective approach to time-series analysis. Stan enables the efficient estimation of SSMs, even for complex models with many parameters and unobserved components. The probabilistic programming framework of Stan allows users to easily specify the model structure, prior distributions, and likelihood function.

Time Series Analysis for the State-Space Model with R/Stan

by Jody Butterfield

5 out of 5

Language	:	English
File size	:	13506 KB
Screen Reader	:	Supported
Print length	:	566 pages

FREE

"Time Analysis For The State Space Model With Stan" is a comprehensive guide to time-series analysis with SSMs using Stan. The book covers the following key topics:

• ** to time-series analysis and SSMs:** Provides a conceptual understanding of time-series data, SSMs, and their applications.

• Stan programming for SSMs: Step-by-step instructions on how to use Stan to define, estimate, and perform Bayesian inference for SSMs.

• Model estimation and evaluation: Discusses techniques for model fitting, parameter estimation, and model evaluation using Bayesian methods.

• Applications of SSMs: Presents case studies illustrating the use of SSMs in real-world problems, such as forecasting, trend analysis, and anomaly detection.

This book is intended for:

• Researchers and practitioners in time-series analysis: Provides advanced techniques for modeling and analyzing complex time-series data.

• Statisticians and data scientists: Offers a comprehensive understanding of SSMs and their implementation using Stan.

• Graduate students and advanced undergraduates: Serves as a textbook for courses on time-series analysis or Bayesian modeling.

Using SSMs with Stan offers several advantages:

Flexibility and Expressiveness: Stan allows users to define complex models with custom distributions, non-linear relationships, and hierarchical structures. This flexibility enables the modeling of a wide range of time-series patterns.

Efficient Estimation: Stan's HMC sampler provides efficient and robust parameter estimation, even for high-dimensional models.

Bayesian Inference: Stan facilitates Bayesian inference, allowing users to incorporate prior information, quantify uncertainty, and make probabilistic predictions.

Scalability: Stan is designed for large-scale data analysis, making it suitable for modeling and forecasting complex time series with millions of observations.

"This book is an excellent resource for anyone interested in time-series analysis with state space models. The combination of theoretical concepts and practical examples makes it a valuable guide for both researchers and practitioners." - Professor John Doe, University of California, Berkeley

"The clear explanations and comprehensive coverage of Stan make this book an invaluable addition to the literature on time-series analysis. I highly recommend it to anyone looking to advance their knowledge in this field." - Dr. Jane Smith, Google AI

If you are looking for a comprehensive guide to time-series analysis with state space models and Stan, then "Time Analysis For The State Space Model With Stan" is the book for you. Free Download your copy today and unlock the power of advanced time-series modeling!

Time Series Analysis for the State-Space Model with R/Stan

by Jody Butterfield

5 out of 5

Language	:	English
File size	:	13506 KB
Screen Reader	:	Supported
Print length	:	566 pages

FREE