Time Analysis for the State Space Model with Stan

Time series analysis is a branch of statistics that deals with the analysis of data that is collected over time. Time series data can be used to track trends, predict future values, and identify patterns.

The state space model is a popular time series model that is used to model systems that evolve over time. State space models are often used to model financial data, weather data, and other types of time series data.

Stan is a probabilistic programming language that can be used to fit and analyze state space models. Stan makes it easy to specify and fit complex statistical models, and it provides a variety of tools for analyzing the results.

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 series analysis is the study of data that is collected over time. Time series data can be used to track trends, predict future values, and identify patterns.

There are a variety of different time series analysis techniques. Some of the most common techniques include:

• Smoothing: Smoothing techniques can be used to remove noise from time series data. This can make it easier to identify trends and patterns.
• Forecasting: Forecasting techniques can be used to predict future values of a time series. This can be useful for planning and decision-making.
• Trend analysis: Trend analysis can be used to identify long-term trends in time series data. This can be useful for understanding how a system is changing over time.

The state space model is a popular time series model that is used to model systems that evolve over time. State space models are often used to model financial data, weather data, and other types of time series data.

The state space model consists of two parts:

• The state equation: The state equation describes how the state of the system evolves over time.
• The observation equation: The observation equation describes how the observations are related to the state of the system.

The state equation is typically a linear equation, while the observation equation can be linear or nonlinear.

Stan is a probabilistic programming language that can be used to fit and analyze state space models. Stan makes it easy to specify and fit complex statistical models, and it provides a variety of tools for analyzing the results.

To fit a state space model in Stan, you need to specify the state equation, the observation equation, and the prior distributions for the model parameters. Stan will then use a sampling algorithm to fit the model to the data.

Once the model has been fit, you can use Stan to analyze the results. Stan provides a variety of tools for visualizing the results, including plots of the state estimates, the observation estimates, and the posterior distributions of the model parameters.

Time series analysis is a powerful tool for understanding data that is collected over time. State space models are a popular time series model that is used to model systems that evolve over time. Stan is a probabilistic programming language that can be used to fit and analyze state space models.

By using Stan to fit and analyze state space models, you can gain valuable insights into how your system evolves over time. This information can be used to improve decision-making, planning, and forecasting.

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