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

• Amazon
• Packt Publishing

• 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

• 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

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.

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

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

My PR 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.