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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 (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 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

Summary


In this chapter, we learned about what is forecasting and why it is needed in a business. Forecasting helps to identify the demand and take necessary steps, as well as in other domains it helps to predict weather, and so on. The decision-making process is highly affected by the results of forecasting. Time Series is the arrangement of insights, typically gathered at standard intervals. It has been used in various domains such as medical, weather, finance markets, and so on.

We also learned about the different types of models and how to analyze trends in Time Series. We also took into consideration seasonality effects on the Time series analysis. We discussed ARIMA in detail and also explored the Time Series library of Julia.

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