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Machine Learning for Time-Series with Python

You're reading from   Machine Learning for Time-Series with Python Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Length 370 pages
Edition 1st Edition
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Author (1):
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Ben Auffarth Ben Auffarth
Author Profile Icon Ben Auffarth
Ben Auffarth
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Time-Series with Python 2. Time-Series Analysis with Python FREE CHAPTER 3. Preprocessing Time-Series 4. Introduction to Machine Learning for Time-Series 5. Forecasting with Moving Averages and Autoregressive Models 6. Unsupervised Methods for Time-Series 7. Machine Learning Models for Time-Series 8. Online Learning for Time-Series 9. Probabilistic Models for Time-Series 10. Deep Learning for Time-Series 11. Reinforcement Learning for Time-Series 12. Multivariate Forecasting 13. Other Books You May Enjoy
14. Index

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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