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Applying Math with Python

You're reading from   Applying Math with Python Over 70 practical recipes for solving real-world computational math problems

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618370
Length 376 pages
Edition 2nd Edition
Languages
Concepts
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Author (1):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: An Introduction to Basic Packages, Functions, and Concepts 2. Chapter 2: Mathematical Plotting with Matplotlib FREE CHAPTER 3. Chapter 3: Calculus and Differential Equations 4. Chapter 4: Working with Randomness and Probability 5. Chapter 5: Working with Trees and Networks 6. Chapter 6: Working with Data and Statistics 7. Chapter 7: Using Regression and Forecasting 8. Chapter 8: Geometric Problems 9. Chapter 9: Finding Optimal Solutions 10. Chapter 10: Improving Your Productivity 11. Index 12. Other Books You May Enjoy

Using Prophet to model time series data

The tools we have seen so far for modeling time series data are very general and flexible methods, but they require some knowledge of time series analysis in order to be set up. The analysis needed to construct a good model that can be used to make reasonable predictions for the future can be intensive and time-consuming, and may not be viable for your application. The Prophet library is designed to automatically model time series data quickly, without the need for input from the user, and make predictions for the future.

In this recipe, we will learn how to use Prophet to produce forecasts from a sample time series.

Getting ready

For this recipe, we will need the Pandas package imported as pd, the Matplotlib pyplot package imported as plt, and the Prophet object from the Prophet library, which can be imported using the following command:

from prophet import Prophet

Prior to version 1.0, the prophet library was called fbprophet...

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