Bayesian Forecasting of Bounded Poisson Distributed Time Series
Feng-Chi Liu,
Cathy W. S. Chen,
Cheng-Ying Ho
Abstract:This research models and forecasts bounded ordinal time series data that can appear in various contexts, such as air quality index (AQI) levels, economic situations, and credit ratings. This class of time series data is characterized by being bounded and exhibiting a concentration of large probabilities on a few categories, such as states 0 and 1. We propose using Bayesian methods for modeling and forecasting in zero-one-inflated bounded Poisson autoregressive (ZOBPAR) models, which are specifically designed t… Show more
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