Forecasting Analogous Time Series

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 similar, time-based patterns. The co-variation of analogous time series is a largely untapped source of information that can improve forecast accuracy (and explainability).This paper takes the Bayesian pooling approach to drawing information from analogous time series to model and forecast a given time series. Bayesian pooling uses data from analogous time series as multiple observations per time period in a group-level model. It then combines estimated parameters of the group model with conventional time series model parameters, using “shrinkage” weights estimated empirically from the data. Major benefits of this approach are that it 1) minimizes the number of parameters to be estimated (many other pooling approaches suffer from too many parameters to estimate), 2) builds on conventional time series models already familiar to forecasters, and 3) combines time series and cross-sectional perspectives in flexible and effective ways.Provided are the necessary terms, concepts, and methods to understand Bayesian pooling and the conditions under which we can expect it to have comparative advantages over conventional time series methods. Useful for both practitioners and researchers are requirements stated on experimental data, treatments, and factors for comparative research on forecast accuracy of pooling methods. Lastly, the paper presents basic principles for applying pooling methods and supporting empirical results. The prospect for automatic pooling methods is good, although the best pooled forecasts at the current state of art will depend on expert judgment and manual interventions for time series that have frequent pattern changes.