https://github.com/PacktPublishing/Machine-Learning-for-Time-Series-with-Python

Machine Learning for Time-Series with Python.Published by Packt
https://github.com/PacktPublishing/Machine-Learning-for-Time-Series-with-Python

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Machine Learning for Time-Series with Python.Published by Packt

• Host: GitHub
• URL: https://github.com/PacktPublishing/Machine-Learning-for-Time-Series-with-Python
• Owner: PacktPublishing
• License: mit
• Created: 2021-04-07T10:45:17.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-05-18T17:42:24.000Z (8 months ago)
• Last Synced: 2024-08-02T14:09:47.191Z (5 months ago)
• Language: Jupyter Notebook
• Size: 8.94 MB
• Stars: 251
• Watchers: 11
• Forks: 152
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-time-series - repo with code

README

        


# Machine-Learning-for-Time-Series-with-Python

[](https://www.amazon.com/Machine-Learning-Time-Python-state/dp/1801819629/)

Become proficient in deriving insights from time-series data and analyzing a model’s performance

## Links

* [Amazon](https://www.amazon.com/Machine-Learning-Time-Python-state/dp/1801819629/)

* [Packt Publishing](https://www.packtpub.com/product/machine-learning-for-time-series-with-python/9781801819626)

## Key Features

* Explore popular and modern machine learning methods including the latest online and deep learning algorithms

* Learn to increase the accuracy of your predictions by matching the right model with the right problem

* Master time-series via real-world case studies on operations management, digital marketing, finance, and healthcare

## What you will learn

- Understand the main classes of time-series and learn how to detect outliers and patterns

- Choose the right method to solve time-series problems

- Characterize seasonal and correlation patterns through autocorrelation and statistical techniques

- Get to grips with time-series data visualization

- Understand classical time-series models like ARMA and ARIMA

- Implement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning models

- Become familiar with many libraries like Prophet, XGboost, and TensorFlow

## Who This Book Is For

This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.

## Table of Contents

1. Introduction to Time-Series with Python

2. Time-Series Analysis with Python

3. Preprocessing Time-Series

4. Introduction to Machine Learning for Time-Series

5. Forecasting with Moving Averages and Autoregressive Models

6. Unsupervised Methods for Time-Series

7. Machine Learning Models for Time-Series 

8. Online Learning for Time-Series

9. Probabilistic Models for Time-Series

10. Deep Learning for Time-Series

11. Reinforcement Learning for Time-Series

12. Multivariate Forecasting

## Author Notes

I've heard from a few people struggling with tsfresh and featuretools for chapter 3.

[My PR](https://github.com/blue-yonder/tsfresh/pull/912) for tsfresh was merged mid-December fixing a version incompatibility - featuretools went through many breaking changes with the release of version 1.0.0 (congratulations to the team!). Please see how to fix any problems in the [discussion here](https://github.com/PacktPublishing/Machine-Learning-for-Time-Series-with-Python/issues/2).

### Download a free PDF

 If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

 https://packt.link/free-ebook/9781801819626