Remove 2008 Remove Modeling Remove Statistics Remove Uncertainty
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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Quantification of forecast uncertainty via simulation-based prediction intervals. Calendaring was therefore an explicit feature of models within our framework, and we made considerable investment in maintaining detailed regional calendars.

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Fitting Bayesian structural time series with the bsts R package

The Unofficial Google Data Science Blog

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

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Data Science, Past & Future

Domino Data Lab

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. Key highlights from the session include. Transcript. Tukey did this paper.