Book Image

Machine Learning for Time-Series with Python

By : Ben Auffarth

Book Image

Machine Learning for Time-Series with Python

By: Ben Auffarth

Overview of this book

The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time 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 time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. 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 time-series.

Preface

Who this book is for

What this book covers

To get the most out of this book

Introduction to Time-Series with Python

Introduction to Time-Series with Python

What Is a Time-Series?

Time-Series and Forecasting – Past and Present

Python for Time-Series

Free Chapter

Time-Series Analysis with Python

Time-Series Analysis with Python

What is time-series analysis?

Working with time-series in Python

Understanding the variables

Uncovering relationships between variables

Identifying trend and seasonality

Preprocessing Time-Series

Preprocessing Time-Series

What Is Preprocessing?

Feature Transforms

Feature Engineering

Python Practice

Introduction to Machine Learning for Time-Series

Introduction to Machine Learning for Time-Series

Machine learning with time-series

Machine learning workflow

Machine learning algorithms for time-series

Forecasting with Moving Averages and Autoregressive Models

Forecasting with Moving Averages and Autoregressive Models

What are classical models?

Python libraries

Python practice

Unsupervised Methods for Time-Series

Unsupervised Methods for Time-Series

Unsupervised methods for time-series

Anomaly detection

Change point detection

Python practice

Machine Learning Models for Time-Series

Machine Learning Models for Time-Series

More machine learning methods for time-series

K-nearest neighbors with dynamic time warping

Gradient boosting

Python exercise

Online Learning for Time-Series

Online Learning for Time-Series

Online learning for time-series

Adaptive learning methods

Python practice

Probabilistic Models for Time-Series

Probabilistic Models for Time-Series

Probabilistic Models for Time-Series

Bayesian Structural Time-Series Models

Python Exercise

Deep Learning for Time-Series

Deep Learning for Time-Series

Introduction to deep learning

Deep learning for time-series

Python practice

Reinforcement Learning for Time-Series

Reinforcement Learning for Time-Series

Introduction to reinforcement learning

Reinforcement Learning for Time-Series

Bandit algorithms

Deep Q-Learning

Python Practice

Multivariate Forecasting

Multivariate Forecasting

Forecasting a Multivariate Time-Series

What's next for time-series?

Other Books You May Enjoy

Other Books You May Enjoy

Index

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What Is Preprocessing?

Anyone who's ever worked in a company on a machine learning project knows that real-world data is messy. It's often aggregated from multiple sources or using multiple platforms or recording devices, and it's incomplete and inconsistent. In preprocessing, we want to improve the data quality to successfully apply a machine learning model.

Data preprocessing includes the following set of techniques:

Feature transforms
- Scaling
- Power/log transforms
- Imputation
Feature engineering

These techniques fall largely into two classes: either they tailor to the assumptions of the machine learning algorithm (feature transforms) or they are concerned with constructing more complex features from multiple underlying features (feature engineering). We'll only deal with univariate feature transforms, transforms that apply to one feature at a time. We won't discuss multivariate feature...