BayesianStructuralTimeSeriesModels

The Bayesian Structural Time Series (BSTS) model is a flexible and powerful framework for modeling time series data. It is a Bayesian approach that uses state space models to decompose a time series into different components such as trend, seasonality, and noise. The model captures non-linear and non-stationary patterns in the data and provides a probabilistic framework for forecasting and anomaly detection.

Pros:

• Flexibility: The BSTS model allows for the inclusion of multiple components and can handle complex time series patterns.
• Uncertainty quantification: Being a Bayesian model, BSTS provides a probabilistic framework that allows for the quantification of uncertainties in the forecasts.
• Anomaly detection: BSTS can identify anomalies by detecting outliers in the observed data that deviate from the predicted values.

Cons:

• Computational complexity: Implementing and fitting BSTS models can be computationally expensive, especially for large and complex time series.
• Model selection: Choosing the appropriate components and priors for the BSTS model requires domain knowledge and expertise.
• Interpretability: Interpreting the results of the BSTS model can be challenging, especially for those without a solid understanding of state space models.

• Demand forecasting: BSTS models can be used to forecast demand for products or services, enabling businesses to optimize inventory management and production planning.
• Financial market analysis: BSTS models can capture trends and cyclic patterns in financial market data and assist in predicting future movements in stock prices or market indices.
• Anomaly detection: BSTS models can be used to identify unusual patterns or outliers in time series data, making it useful for detecting anomalies in sensor readings, network traffic, or fraud detection.

• Bayesian Structural Time Series: The official documentation of the bsts package in R provides a thorough introduction to Bayesian structural time series modeling.
• Probabilistic Programming and Bayesian Methods for Hacker: This GitHub repository contains a comprehensive book on Bayesian methods and probabilistic programming, including a chapter on Bayesian time series analysis with useful code examples.
• Anomaly Detection using Bayesian Structural Time Series and Prophet: A tutorial on anomaly detection using BSTS and the Prophet library. The article provides step-by-step implementation guidance.

• Sean J. Taylor: Sean Taylor is a leading researcher in the field of Bayesian time series modeling and has contributed extensively to the development of BSTS methods.
• Eric Novik: Eric Novik is a data scientist and researcher who has expertise in Bayesian time series models, including BSTS. His GitHub page contains various projects and code related to BSTS.
• Oscar Struyve: Oscar Struyve is a data scientist and developer who has worked on implementing BSTS models for time series analysis. His GitHub repository includes several BSTS projects.
• Harrison D. Edwards: Harrison Edwards has expertise in Bayesian time series modeling, including BSTS. His GitHub page contains code examples and projects related to BSTS.
• Austin Rochford: Austin Rochford is a Bayesian statistician and data scientist with expertise in time series analysis. His GitHub page includes BSTS-related projects and code.

• Data Type : TimeSeriesData
• Problem type : AnomalyDetection