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

Mather, Bob

ISBN 10: 1393147380 / ISBN 13: 9781393147381

Edité par Abiprod Pty Ltd, 2020

A propos de cet article

PRINT ON DEMAND pp. 164.

N° de réf. du vendeur 18384510572

Détails bibliographiques

• Titre: Time Series with Python: How to Implement Time Series Analysis and Forecasting Using Python
• Éditeur: Abiprod Pty Ltd
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 1393147380
• ISBN à 13 chiffres: 9781393147381
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

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.