Bayesian forecasting of many count-valued time series
arXiv:1805.05232 · doi:10.1080/07350015.2019.1604372
Abstract
This paper develops forecasting methodology and application of new classes of dynamic models for time series of non-negative counts. Novel univariate models synthesise dynamic generalized linear models for binary and conditionally Poisson time series, with dynamic random effects for over-dispersion. These models allow use of dynamic covariates in both binary and non-zero count components. Sequential Bayesian analysis allows fast, parallel analysis of sets of decoupled time series. New multivariate models then enable information sharing in contexts when data at a more highly aggregated level provide more incisive inferences on shared patterns such as trends and seasonality. A novel multi-scale approach-- one new example of the concept of decouple/recouple in time series-- enables information sharing across series. This incorporates cross-series linkages while insulating parallel estimation of univariate models, hence enables scalability in the number of series. The major motivating context is supermarket sales forecasting. Detailed examples drawn from a case study in multi-step forecasting of sales of a number of related items showcase forecasting of multiple series, with discussion of forecast accuracy metrics and broader questions of probabilistic forecast accuracy assessment.
26 pages, 10 figures, 1 table
References in corpus (1)
Cited by in corpus (8)
- Probabilistic forecasting of heterogeneous consumer transaction-sales time series
- Bayesian forecasting of multivariate time series: Scalability, structure uncertainty and decisions
- Forecasting: theory and practice
- Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models
- Bayesian Dynamic Modeling and Monitoring of Network Flows
- Bayesian Computation in Dynamic Latent Factor Models
- Warped Dynamic Linear Models for Time Series of Counts
- Methods to Compute Prediction Intervals: A Review and New Results