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Julia Programming Projects

You're reading from   Julia Programming Projects Learn Julia 1.x by building apps for data analysis, visualization, machine learning, and the web

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788292740
Length 500 pages
Edition 1st Edition
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Author (1):
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Adrian Salceanu Adrian Salceanu
Author Profile Icon Adrian Salceanu
Adrian Salceanu
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Julia Programming FREE CHAPTER 2. Creating Our First Julia App 3. Setting Up the Wiki Game 4. Building the Wiki Game Web Crawler 5. Adding a Web UI for the Wiki Game 6. Implementing Recommender Systems with Julia 7. Machine Learning for Recommender Systems 8. Leveraging Unsupervised Learning Techniques 9. Working with Dates, Times, and Time Series 10. Time Series Forecasting 11. Creating Julia Packages 12. Other Books You May Enjoy

Time series stationarity


A time series is considered stationary if its statistical properties such as mean, variance, autocorrelation, and so on, are constant over time. Stationarity is important because most forecasting models run on the assumption that the time series is stationary or can be rendered (approximately) stationary using transformations. The reason for this approach is that values in a stationary time series are much easier to predict—if its properties are constant, we can simply state that they will be in the future as they were in the past. Once we forecast future values based on stationary time series, we can then reverse the process and the transformations to compute the values that would match the original series.

Thus, the properties of a stationary time series do not depend on the time when the series is observed. Implicitly, this means that time series that present seasonality or trends are not stationary. In this context, again, we must be careful of the difference...

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