Bayesian hierarchical model with Dirichlet process learns latent heterogeneity in innovation rates of count time series and shows favorable forecasting on Pittsburgh crime data.
17 Table 1: Mean absolute deviations for one-step-ahead predictions
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Learning a latent pattern of heterogeneity in the innovation rates of a time series of counts
Bayesian hierarchical model with Dirichlet process learns latent heterogeneity in innovation rates of count time series and shows favorable forecasting on Pittsburgh crime data.