Machine Learning for Time-Series with Python: Forecast, predict and detect anomalies with state-of-the-art machine learning

[LeeAndro]

English | 2021 | ISBN: ‎ 1801819629 | 371 pages | True PDF EPUB | 29.06 MB​

Get better insights from -series data and become proficient in model performance analysis
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 series via real-world case studies on operations management, digital marketing, finance, and healthcare

Book Description

The Python -series ecosystem is huge and often quite hard to get a good grasp on, especially for -series since there are so many new libraries and new models. This book aims to deepen your understanding of series by providing a comprehensive overview of popular Python -series packages and help you build better predictive systems.

Machine Learning for -Series with Python starts by re-introducing the basics of series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading -series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature eeering.

This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at real-world case studies covering weather, traffic, biking, and stock market data.

By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to -series.
What you will learn

Understand the main classes of series and learn how to detect outliers and patterns
Choose the right method to solve -series problems
Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
Get to grips with -series data visualization
Understand classical -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 want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.
Table of Contents

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



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