A0677
Title: Dynamic hierarchical state space forecasting
Authors: Ziyue Liu - Indiana University School of Medicine (United States) [presenting]
Abstract: Situations are considered when there are time series data from multiple units that share similar patterns when aligned in terms of an internal time. Internal time is defined as an index according to evolving features of interest. When mapped back to the calendar time, these time series can span different time intervals that can include the future calendar time of the targeted unit, over which the information can be borrowed from other units in forecasting the targeted unit. First, a hierarchical state space model is built for the multiple time series data in terms of the internal time, where the shared components capture the similarities among different units while allowing for unit-specific deviations. A conditional state space model is then constructed to incorporate the information of existing units as the prior information in forecasting the targeted unit. The information from the other units and the unit's history is incorporated by running the Kalman filtering based on the conditional state space model on the targeted unit. The forecasts are then transformed from internal time back into calendar time for ease of interpretation. A simulation study is conducted to evaluate the finite sample performance. Forecasting state-level new COVID-19 cases in the USA is used for illustration.
