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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 forecasting?


Let's take the example of an organization that needs to find out the demand for its inventory in the near future, to maximize the return on investment.

For instance, numerous stock frameworks apply for indeterminate demand. The stock parameters in these frameworks require evaluations of the demand and forecast error distributions.

The two phases of these frameworks, forecasting and stock control, are frequently analyzed autonomously. It is essential to comprehend the cooperation between demand estimating and stock control since this impacts the execution of the stock framework.

Forecasting requirements include:

  • Each decision gets to be operational sooner or later, so it ought to be based on figures of future conditions.

  • Figures are required all through an organization and they should absolutely not be created by a disconnected gathering of forecasters.

  • Forecasting is never "wrapped up". Forecasts are required constantly, and as time proceeds onward, the effect of the forecasts...

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