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Climate change predictions: Anticipating and adapting to a warming world

IBM Big Data Hub

According to the Geophysical Fluid Dynamics Laboratory of the US’s National Oceanic and Atmospheric Association (NOAA), “Climate models reduce the uncertainty of climate change impacts, which aids in adaptation.” 3 Climate Science Special Report: Fourth National Climate Assessment, Volume I. degrees Celsius (2.66

Modeling 122
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Perform time series forecasting using Amazon Redshift ML and Amazon Forecast

AWS Big Data

Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more accurate financial, marketing, and operational decisions. and Karra Taniskidou, E.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

This blog post discusses such a comprehensive approach that is used at Youtube. Crucially, it takes into account the uncertainty inherent in our experiments. In this section we’ll discuss how we approach these two kinds of uncertainty with QCQP. And we can keep repeating this approach, relying on intuition and luck.

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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

Quantification of forecast uncertainty via simulation-based prediction intervals. Facebook in a recent blog post unveiled Prophet , which is also a regression-based forecasting tool. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. Accessed on 20 March 2017. OTexts, 2014.

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Cloudera + Hortonworks, from the Edge to AI

Cloudera

This communication contains forward-looking statements within the meaning of the federal securities law that are subject to various risks and uncertainties that could cause our actual results to differ materially from those expressed or implied in such statements. Forward-Looking Statements.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. From a Bayesian perspective, one can combine joint posterior samples for $E[Y_i | T_i=t, E_i=j]$ and $P(E_i=j)$, which provides a measure of uncertainty around the estimate. ACM, 2017. [4] 5] Imbens, Guido W.,

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Fitting Bayesian structural time series with the bsts R package

The Unofficial Google Data Science Blog

Time series data are having something of a moment in the tech blogs right now, with Facebook announcing their "Prophet" system for time series forecasting (Taylor and Letham 2017), and Google posting about its forecasting system in this blog (Tassone and Rohani 2017). Forecasting (e.g. Goldfarb, S. Greenstein, and C.