A1006
Title: Multivariate interval-valued time series data analysis
Authors: S Yaser Samadi - Southern Illinois University Carbondale (United States) [presenting]
Abstract: Interval-valued time series (ITS) data are prevalent in many scientific disciplines and applications, including economics, finance, social sciences, and meteorology. Such data arise naturally or through the aggregation of large datasets. Modeling and forecasting of multivariate ITS data have gained significant attention in statistics and related fields. Despite existing efforts in the literature, substantial gaps remain in both theory frameworks and practical applications for appropriately analyzing these data. A new class of interval-valued vector autoregressive (IVAR) models is introduced to capture the cross-dependence dynamics within an ITS vector system. The maximum likelihood estimators of the parameters of the IVAR models are derived, and their asymptotic properties are established. Simulation studies and real data analyses are presented to demonstrate and validate the effectiveness and practical utility of the proposed methodology.
