Time Series Analysis with Python Cookbook
This book will show you how to implement practical Python solutions for time series analysis and anomaly detection. As you progress, you’ll be able to extract insights and forecast using statistical, machine learning, and deep learning models.
Offered by
Difficulty Level
Intermediate
Completion Time
21h
Language
English
About Book
Who Is This Book For?
This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.
Time Series Analysis with Python Cookbook
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 21h total length
Getting Started with Time Series Analysis
Reading Time Series Data from Files
Reading Time Series Data from Databases
Persisting Time Series Data to Files
Persisting Time Series Data to Databases
Working with Date and Time in Python
Handling Missing Data
Outlier Detection Using Statistical Methods
Exploratory Data Analysis and Diagnosis
Building Univariate Time Series Models Using Statistical Methods
Additional Statistical Modeling Techniques for Time Series
Forecasting Using Supervised Machine Learning
Deep Learning for Time Series Forecasting
Outlier Detection Using Unsupervised Machine Learning
Advanced Techniques for Complex Time Series
Related Resources
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