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.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
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
Summary
Free Chapter
2
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
Summary
3
Preprocessing Time-Series
Preprocessing Time-Series
What Is Preprocessing?
Feature Transforms
Feature Engineering
Python Practice
Summary
4
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
Summary
5
Forecasting with Moving Averages and Autoregressive Models
Forecasting with Moving Averages and Autoregressive Models
What are classical models?
Python libraries
Python practice
Summary
6
Unsupervised Methods for Time-Series
Unsupervised Methods for Time-Series
Unsupervised methods for time-series
Anomaly detection
Change point detection
Clustering
Python practice
Summary
7
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
Silverkite
Gradient boosting
Python exercise
Summary
8
Online Learning for Time-Series
Online Learning for Time-Series
Online learning for time-series
Drift
Adaptive learning methods
Python practice
Summary
9
Probabilistic Models for Time-Series
Probabilistic Models for Time-Series
Probabilistic Models for Time-Series
Prophet
Markov Models
Fuzzy Modeling
Bayesian Structural Time-Series Models
Python Exercise
Summary
10
Deep Learning for Time-Series
Deep Learning for Time-Series
Introduction to deep learning
Deep learning for time-series
Python practice
Summary
11
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
Summary
12
Multivariate Forecasting
Multivariate Forecasting
Forecasting a Multivariate Time-Series
What's next for time-series?
13
Other Books You May Enjoy
Other Books You May Enjoy
14
Index
Index

Python practice

The installation in this chapter is very simple, since, in this chapter, we'll only use River. We can quickly install it from the terminal (or similarly from Anaconda Navigator):

pip install river

We'll execute the commands from the Python (or IPython) terminal, but equally, we could execute them from a Jupyter notebook (or a different environment).

Drift detection

Let's start off by trying out drift detection with an artificial time-series. This follows the example in the tests of the River library.

We'll first create an artificial time-series that we can test:

import numpy as np
np.random.seed(12345)
data_stream = np.concatenate(
    (np.random.randint(2, size=1000), np.random.randint(8, size=1000))
)

This time-series is composed of two series that have different characteristics. Let's see how quickly the drift detection algorithms pick up on this.

Running the drift detector over this means iterating over...

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