-
A Comprehensive Hidden Markov Model for Hourly Rainfall Time Series
For hydrological applications, such as urban flood modelling, it is ofte...
read it
-
Bivariate modelling of precipitation and temperature: Bivariate modelling of precipitation and temperature using a non-homogeneous hidden Markov model
Aiming to generate realistic synthetic times series of the bivariate pro...
read it
-
Bivariate modelling of precipitation and temperature using a non-homogeneous hidden Markov model
Aiming to generate realistic synthetic times series of the bivariate pro...
read it
-
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series
We consider the problem of modeling discrete-valued vector time series d...
read it
-
Modeling rainfalls using a seasonal hidden markov model
In order to reach the supply/demand balance, electricity providers need ...
read it
-
Bayesian Approximations to Hidden Semi-Markov Models
We propose a Bayesian hidden Markov model for analyzing time series and ...
read it
-
The conditionally autoregressive hidden Markov model (CarHMM): Inferring behavioural states from animal tracking data exhibiting conditional autocorrelation
One of the central interests of animal movement ecology is relating move...
read it
An Advanced Hidden Markov Model for Hourly Rainfall Time Series
For hydrological applications, such as urban flood modelling, it is often important to be able to simulate sub-daily rainfall time series from stochastic models. However, modelling rainfall at this resolution poses several challenges, including a complex temporal structure including long dry periods, seasonal variation in both the occurrence and intensity of rainfall, and extreme values. We illustrate how the hidden Markov framework can be adapted to construct a compelling model for sub-daily rainfall, which is capable of capturing all of these important characteristics well. These adaptations include clone states and non-stationarity in both the transition matrix and conditional models. Set in the Bayesian framework, a rich quantification of both parametric and predictive uncertainty is available, and thorough model checking is made possible through posterior predictive analyses. Results from the model are interpretable, allowing for meaningful examination of seasonal variation and medium to long term trends in rainfall occurrence and intensity. To demonstrate the effectiveness of our approach, both in terms of model fit and interpretability, we apply the model to an 8-year long time series of hourly observations.
READ FULL TEXT
Comments
There are no comments yet.