English | 2020 | ASIN: B0861T6W53 | 222 Pages | ePUB | 4.23 MB
Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks?
This book is your answer.
Update: Thanks for the overwhelming positive feedback. I am glad that you have found the information in this book useful. To address a couple of complaints, we have fixed a few issues with paperback formatting that were mentioned. Also, we have to mention that we have had to add links to external datasets in the book for our examples. If we added 1000's of sample data points in the book without a link, it would be take up 100's of pages, and would take away from the relevance of the code.
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.

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