Bayesian Forecasting of Many Count-Valued Time Series
Bibliographic Data
| ID | 19418408 |
|---|---|
| Authors | Lindsay R Berry (0009-0003-4782-2570, Berry Consultants LLC, 3345 Bee Caves Road, Suite 201, Austin, TX 78746 ()), Mike West (0000-0002-7297-7801, Duke University) |
| Year | 2020 |
| Volume | 38 |
| Issue | 4 |
| Pages | 872-887 |
| Publication date | 2020-10-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2019.1604372 |
| OpenAlex | W2800442498 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
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
Bayesian probability · Count data · Data mining · Econometrics · Machine learning · Multivariate statistics · Poisson distribution · Probabilistic logic · Scalability · Statistics · Time series · Univariate · Advanced Statistical Methods and Models · Computer Science · Forecasting Techniques and Applications · Mathematics · Statistical Methods and Bayesian Inference · Artificial Intelligence
| Citations per year | 0.14 |
|---|---|
| Citation span | 2019 - 2019 (1) |
| Highly cited | No |