A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...
Scientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combi...
Due to its nonlinear nature, the climate system shows quite high natural variability on different time scales, including multiyear oscillations such as the El Ni~no Southern Oscill...
The research field of transportation demand forecasting has started to focus on disaggregate travel behavior and micro-simulation models. To create data infrastructure, disaggrega...
Ali Frihida, Danielle J. Marceau, Marius Thé...
ARIMA is a popular method to analyze stationary univariate time series data. There are usually three main stages to build an ARIMA model, including model identification, model est...