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
Introduction to Time-Series with Python
Time-Series Analysis with Python
Preprocessing Time-Series
Introduction to Machine Learning for Time-Series
Forecasting with Moving Averages and Autoregressive Models
Unsupervised Methods for Time-Series
Machine Learning Models for Time-Series
Online Learning for Time-Series
Probabilistic Models for Time-Series
Deep Learning for Time-Series
Reinforcement Learning for Time-Series
Multivariate Forecasting
Author Notes
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.
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