Conference Agenda

Research on count time series has grown substantially, leading to the development of numerous models designed to capture key characteristics such as trends, seasonality, overdispersion, outliers, and complex dependence structures. Despite these advances, the evaluation of such models remains challenging due to the limited availability of real-world count time series. This scarcity often forces researchers to illustrate new methods using only a few datasets, which restricts systematic comparison and hinders robust performance assessment. Addressing this gap is essential for advancing methodological development and ensuring practical applicability in diverse domains.
In this presentation, we focus on the generation of synthetic count time series that can exhibit a wide range of controlled behaviors. Our approach relies on Mixture INAR (MixINAR) models and a set of descriptive features designed to summarize key properties of count data. We first discuss which features are most suitable for characterizing count time series and then show how they can be used to guide the simulation of diverse collections of synthetic data. By comparing the simulated series with commonly used benchmark datasets, we illustrate how realistic and flexible the generated time series can be. Overall, this work provides a practical way to create representative collections of count time series, supporting more thorough testing and comparison of models in this area.

This work is financed by National Funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P. (Portuguese Foundation for Science and Technology) within the project TSP2Net, with reference 2023.13039.PEX, https://doi.org/10.54499/2023.13039.PEX