2021/09/21 by Shirley Rojas‐Salazar, Erin M. Schliep, Rojas-Salazar, Shirley +9
Computer Science · #Applications (stat.AP) #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2109.09949
openalex publication_date 2021/09/21 · openalex created_date 2021/09/27 · openalex updated_date 2026/07/28
Environmental time series data observed at high frequencies can be studied with approaches such as hidden Markov and semi-Markov models (HMM and HSMM). HSMMs extend the HMM by explicitly modeling the time spent in each state. In a discrete-time HSMM, the duration in each state can be modeled with a zero-truncated Poisson distribution, where the duration parameter may be state-specific but constant in time. We extend the HSMM by allowing the state-specific duration parameters to vary in time and model them as a function of known covariates observed over a period of time leading up to a state transition. In addition, we propose a data subsampling approach given that high-frequency data can violate the conditional independence assumption of the HSMM. We apply the model to high-frequency data collected by an instrumented buoy in Lake Mendota. We model the phycocyanin concentration, which is used in aquatic systems to estimate the relative abundance of blue-green algae, and identify important time-varying effects associated with the duration in each state.