Forecasting Analogous Time Series

Abstract

Organizations that use time-series forecasting regularly, generally use it for many products or services. Among the variables they forecast are groups of analogous time series (series that follow similar, time-based patterns). Their covariation is a largely untapped source of information that can improve forecast accuracy. We take the Bayesian pooling approach to drawing information from analogous time series to model and forecast a given time series. In using Bayesian pooling, we use data from analogous time series as multiple observations per time period in a group-level model. We then combine estimated parameters of the group model with conventional time-series-model parameters, using so-called weights shrinkage. Major benefits of this approach are that it (1) requires few parameters for estimation; (2) builds directly on conventional time-series models; (3) adapts to pattern changes in time series, providing rapid adjustments and accurate model estimates; and (4) screens out adverse effects of outlier data points on time-series model estimates. For practitioners, we provide the terms, concepts, and methods necessary for a basic understanding of Bayesian pooling and the conditions under which it improves upon conventional time-series methods. For researchers, we describe the experimental data, treatments, and factors needed to compare the forecast accuracy of pooling methods. Last, we present basic principles for applying pooling methods and supporting empirical results. Conditions favoring pooling include time series with high volatility and outliers. Simple pooling methods are more accurate than complex methods, and we recommend manual intervention for cases with few time series.KeywordsAnalogous time seriesBayesian methodsmultiple time seriespooling