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

Bob Mather

ISBN 10: 0648783081 / ISBN 13: 9780648783084

Published by Bob Mather, 2020

About this Item

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.

Seller Inventory # 9780648783084

Bibliographic Details

• Title: Time Series with Python : How to Implement Time Series Analysis and Forecasting Using Python
• Publisher: Bob Mather
• Publication Date: 2020
• Language: English
• ISBN 10: 0648783081
• ISBN 13: 9780648783084
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 12D millimeters 210 millimeters width by 148 millimeters height by 12 millimeters depth
• Weight: 294 grams

About this title

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.