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Time Series Analysis with Python Cookbook

You're reading from Time Series Analysis with Python Cookbook

Product type Book

Published in Jun 2022

Publisher Packt

ISBN-13 9781801075541

Pages 630 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Tarek A. Atwan

Table of Contents (18) Chapters

Preface

Chapter 1: Getting Started with Time Series Analysis

Chapter 2: Reading Time Series Data from Files

Chapter 3: Reading Time Series Data from Databases

Chapter 4: Persisting Time Series Data to Files

Chapter 5: Persisting Time Series Data to Databases

Chapter 6: Working with Date and Time in Python

Chapter 7: Handling Missing Data

Chapter 8: Outlier Detection Using Statistical Methods

Chapter 9: Exploratory Data Analysis and Diagnosis

Chapter 10: Building Univariate Time Series Models Using Statistical Methods

Chapter 11: Additional Statistical Modeling Techniques for Time Series

Chapter 12: Forecasting Using Supervised Machine Learning

Chapter 13: Deep Learning for Time Series Forecasting

Chapter 14: Outlier Detection Using Unsupervised Machine Learning

Chapter 15: Advanced Techniques for Complex Time Series

Index

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Technical requirements

In this chapter and forward, we will be using pandas 1.4.2 (released April 2, 2022) extensively.

Throughout our journey, you will be installing additional Python libraries to use in conjunction with pandas. You can download the Jupyter notebooks from the GitHub repository (https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook./blob/main/code/Ch2/Chapter%202.ipynb) to follow along.

You can download the datasets used in this chapter from the GitHub repository using this link: https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook./tree/main/datasets/Ch2.

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Time Series Analysis with Python Cookbook

Published in: Jun 2022 Publisher: Packt ISBN-13: 9781801075541

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Authors (1)

Tarek A. Atwan

Tarek A. Atwan is a data analytics expert with over 16 years of international consulting experience, providing subject matter expertise in data science, machine learning operations, data engineering, and business intelligence. He has taught multiple hands-on coding boot camps, courses, and workshops on various topics, including data science, data visualization, Python programming, time series forecasting, and blockchain at various universities in the United States. He is regarded as a data science mentor and advisor, working with executive leaders in numerous industries to solve complex problems using a data-driven approach.

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