One important implementation of Bayesian forecasting is the Multi-State Kalman Filter (MSKF) method. It is particularly suited for short and irregular time series data. In certain applications, time series data are available on numerous parallel observational units which, while not having...

Organizations that use time series forecasting on a regular basis generally forecast many variables, such as demand for many products or services. Within the population of variables forecasted by an organization, we can expect that there will be groups of analogous time series that follow...