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Example 1: Nowcasting Scott and Varian (2014, 2015) used structural time series models to show how Google search data can be used to improve short term forecasts ("nowcasts") of economic time series. Figure 1 shows the motivating data set from Scott and Varian (2014), which is also included with the bsts package.
Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. The data contains measurements of electric power consumption in different households for the year 2014. Prepare the data Refer to the following notebook for the steps needed to create this use case.
Quantification of forecast uncertainty via simulation-based prediction intervals. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. OTexts, 2014. 2014): 276. [7] Large-Scale Parallel Statistical Forecasting Computations in R ”, Google Research report. [8]
Crucially, it takes into account the uncertainty inherent in our experiments. There is also uncertainty related to our modeling choices — did we select the correct polynomial embedding function $f(x)$, or is the true relationship better described by a different polynomial embedding?
Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. CoRR, 2014. [2] Technical Report 1341, University of Montreal, 2009.
The IT sector in Ukraine had stabilized after the 2014 Russian incursion with growth accelerating beginning in 2017 and “supercharging” in 2020 and 2021, says Katie Gove, senior director-analyst in Gartner’s Technology and Service Provider Research division. One year later, the industry’s remarkable resilience is clear.
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