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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (17) Chapters Close

Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. The Groundwork – Julia's Environment FREE CHAPTER 2. Data Munging 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

What is TimeSeries?


A time series is an arrangement of insights, typically gathered at standard intervals. Time series information normally happens in numerous applications:

  • Economics: For example, monthly data for unemployment, hospital admissions, and so on

  • Finance: For example, daily exchange rate, a share price, and so on

  • Environmental: For example, daily rainfall, air quality readings, and so on

  • Medicine: For example, ECG brain wave activity every 2 to 8 seconds

The techniques for time series investigation predate those for general stochastic procedures and Markov chains. The goals of time series analysis are to portray and outline time series data, fit low-dimensional models, and to make desirable forecasts.

Trends, seasonality, cycles, and residuals

One straightforward strategy for depicting a series is that of classical disintegration. The idea is that the arrangement can be segmented into four components:

  • Trend (Tt): Long-term movements in the mean

  • Seasonal effects (It): Cyclical fluctuations...

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