Remove 2014 Remove Data Collection Remove Forecasting Remove Reporting
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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

by ERIC TASSONE, FARZAN ROHANI We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. So it should come as no surprise that Google has compiled and forecast time series for a long time.

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Optimizing clinical trial site performance: A focus on three AI capabilities

IBM Big Data Hub

This article, part of the IBM and Pfizer’s series on the application of AI techniques to improve clinical trial performance, focuses on enrollment and real-time forecasting. AI models can be designed to detect anomalies in real-time site performance data.

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Benchmarking Performance: Your Options, Dos, Don'ts and To-Die-Fors!

Occam's Razor

If you want to learn about how to do simple forecasting and trend analysis, please see the official forecast function in Excel post on the Microsoft website, and this handy tutorial on trend lines and forecasting in excel. If you do use them, please consider the data collection methodology. Exhaust those first.

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Making smart cities safer with data

Cloudera

billion in 2014. One such malware is Okiru, a Mirai variant which was reported in January 2018 to be targeting ARC-based processors that are embedded in billions of connected devices. Cost-effectively ingest, store and utilize data from all IoT devices. Analyze all data – be it at the edge, on cloud, on-prem or in a hybrid mode.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

These two points provide a different kind of risk management mechanism which is effective for science, specifically data science. Of course, some questions in business cannot be answered with historical data. Instead they require investment, tooling, and time for data collection.