Machine learning for time-series with Python : forecast, predict, and detect anomalies with state-of-the-art machine learning methods / Ben Auffarth.

Format:

Book

Author/Creator:

Auffarth, Ben, author.

Language:

English

Subjects (All):

Machine learning.

Time-series analysis--Data processing.

Time-series analysis.

Time-series analysis--Computer programs.

Python (Computer program language).

Physical Description:

1 online resource (371 pages)

Place of Publication:

Birmingham : Packt Publishing, Limited, [2021]

Biography/History:

Auffarth Ben: Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph. D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.

Summary:

The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview.

Contents:

Cover

Copyright

Contributors

Table of Contents

Preface

Chapter 1: Introduction to Time Series with Python

What Is a Time Series?

Characteristics of Time Series

Time Series and Forecasting - Past and Present

Demography

Genetics

Astronomy

Economics

Meteorology

Medicine

Applied Statistics

Python for Time Series

Installing libraries

Jupyter Notebook and JupyterLab

NumPy

pandas

Best practice in Python

Summary

Chapter 2: Time-Series Analysis with Python

What is time series analysis?

Working with time series in Python

Requirements

Datetime

Understanding the variables

Uncovering relationships between variables

Identifying trend and seasonality

Chapter 3: Preprocessing Time Series

What Is Preprocessing?

Feature Transforms

Scaling

Log and Power Transformations

Imputation

Feature Engineering

Date- and Time-Related Features

ROCKET

Shapelets

Python Practice

Log and Power Transformations in Practice

Holiday Features

Date Annotation

Paydays

Seasons

The Sun and Moon

Business Days

Automated Feature Extraction

Shapelets in Practice

Chapter 4: Introduction to Machine Learning for Time-Series

Machine learning with time series

Supervised, unsupervised, and reinforcement learning

History of machine learning

Machine learning workflow

Cross-validation

Error metrics for time series

Regression

Classification

Comparing time-series

Machine learning algorithms for time-series

Distance-based approaches

Time Series Forest and Canonical Interval Forest

Symbolic approaches

HIVE-COTE

Discussion

Implementations

Chapter 5: Time-Series Forecasting with Moving Averages and Autoregressive Models.

What are classical models?

Moving average and autoregression

Model selection and order

Exponential smoothing

ARCH and GARCH

Vector autoregression

Python libraries

Statsmodels

Python practice

Modeling in Python

Chapter 6: Unsupervised Methods for Time-Series

Unsupervised methods for time-series

Anomaly detection

Microsoft

Google

Amazon

Facebook

Twitter

Change point detection

Clustering

Chapter 7: Machine Learning Models for Time-Series

More machine learning methods for time series

Validation

K-nearest neighbors with dynamic time warping

Silverkite

Gradient boosting

Python exercise

Virtual environments

K-nearest neighbors with dynamic time warping in Python

Ensembles with Kats

Chapter 8: Online Learning for Time-Series

Online learning for time series

Online algorithms

Drift

Drift detection methods

Adaptive learning methods

Drift detection

Model selection

Chapter 9: Probabilistic Models for Time-Series

Probabilistic Models for Time-Series

Prophet

Markov Models

Fuzzy Modeling

Bayesian Structural Time-Series Models

Python Exercise

Markov Switching Model

Fuzzy Time-Series

Bayesian Structural Time-Series Modeling

Chapter 10: Deep Learning for Time-Series

Introduction to deep learning

Deep learning for time series

Autoencoders

InceptionTime

DeepAR

N-BEATS

Recurrent neural networks

ConvNets

Transformer architectures

Informer

Fully connected network

Recurrent neural network.

Dilated causal convolutional neural network

Chapter 11: Reinforcement Learning for Time-Series

Introduction to reinforcement learning

Reinforcement Learning for Time-Series

Bandit algorithms

Deep Q-Learning

Recommendations

Trading with DQN

Chapter 12: Multivariate Forecasting

Forecasting a Multivariate Time-Series

What's next for time-series?

Why subscribe?

Packt Page

Other Books You May Enjoy

Index.

Notes:

Description based upon print version of record.

Adaptive learning methods.

Description based on print version record.

ISBN:

9781801816106

1801816107

OCLC:

1283853143

Publisher Number:

9781801819626