Time Series with Python : How to Implement Time Series Analysis and Forecasting Using Python

Bob Mather

ISBN 10: 1393147380 / ISBN 13: 9781393147381

Verlag: Draft2digital, 2020

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks This book is your answer. Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python. This book solves all these problems, and more. It starts off with basic concepts in Time Series, and switches to more advanced topics. It shows you how to set up Python from start, and goes through over 20 examples of applying both simple and advanced Time Series concepts with Python code. Here's What's Included In this Book: What is a Time Series 4 Different Elements of a Time Series Why Python is the best way to Implement Time Series Step by Step Guide to Installing Python and Importing Time Series Data 6 Different Techniques to Analyze Time Series Data 3 Advanced Time Series Concepts for Time Series Prediction Time Series Visualization Techniques in Python Even if you've never implemented Time Series before, you will still find this book useful.

Bestandsnummer des Verkäufers 9781393147381

Bibliografische Details

• Titel: Time Series with Python : How to Implement Time Series Analysis and Forecasting Using Python
• Verlag: Draft2digital
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 1393147380
• ISBN-13: 9781393147381
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks?

 

This book is your answer.

 

Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python.

 

This book solves all these problems, and more. It starts off with basic concepts in Time Series, and switches to more advanced topics. It shows you how to set up Python from start, and goes through over 20 examples of applying both simple and advanced Time Series concepts with Python code.

 

Here's What's Included In this Book:

 

 

What is a Time Series?

 

4 Different Elements of a Time Series

 

Why Python is the best way to Implement Time Series

 

Step by Step Guide to Installing Python and Importing Time Series Data

 

6 Different Techniques to Analyze Time Series Data

 

3 Advanced Time Series Concepts for Time Series Prediction

 

Time Series Visualization Techniques in Python

 

 

Even if you've never implemented Time Series before, you will still find this book useful.