You're reading from Machine Learning for Time-Series with Python

Product typeBook

Published inOct 2021

PublisherPackt

ISBN-139781801819626

Edition1st Edition

Concepts

Machine Learning

Author (1)

Ben Auffarth

Silverkite

The Silverkite algorithm ships together with the Greykite library released by LinkedIn. It was explicitly designed with the goals in mind of being fast, accurate, and intuitive. The algorithm is described in a 2021 publication ("A flexible forecasting model for production systems", by Reza Hosseini and others).

According to LinkedIn, it can handle different kinds of trends and seasonalities such as hourly, daily, weekly, repeated events, and holidays, and short-range effects. Within LinkedIn, it is used for both short-term, for example, a 1-day head, and long-term forecast horizons, such as 1 year ahead.

Use cases within LinkedIn include optimizing budget decisions, setting business metric targets, and providing sufficient infrastructure to handle peak traffic. Furthermore, a use case has been to model recoveries from the COVID-19 pandemic.

The time-series is modeled as an additive composite of trends, change points, and seasonality, where seasonality...

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Machine Learning for Time-Series with Python

Published in: Oct 2021Publisher: PacktISBN-13: 9781801819626

Author (1)

Ben Auffarth

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.
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