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

Ben Auffarth

 
9781801819626: Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Softcover
ISBN 10: 1801819629 ISBN 13: 9781801819626
Verlag: Packt Publishing, 2021

Inhaltsangabe

Become proficient in deriving insights from time-series data and analyzing a model’s performance

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 time-series via real-world case studies on operations management, digital marketing, finance, and healthcare

Book Description

Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.

This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. 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.

Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.

By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.

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

  1. Introduction to Time-Series with Python
  2. Time-Series Analysis with Python
  3. Preprocessing Time-Series
  4. Introduction to Machine Learning for Time Series
  5. Forecasting with Moving Averages and Autoregressive Models
  6. Unsupervised Methods for Time-Series
  7. Machine Learning Models for Time-Series
  8. Online Learning for Time-Series
  9. Probabilistic Models for Time-Series
  10. Deep Learning for Time-Series
  11. Reinforcement Learning for Time-Series
  12. Multivariate Forecasting

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

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.

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Verlag
Packt Publishing
Erscheinungsdatum
2021
Sprache
Englisch
ISBN 10 
1801819629
ISBN 13 
9781801819626
Einband
Taschenbuch
Anzahl der Seiten
370
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
gpsr@libri.de
gpsr@libri.de

Friedensallee 273
Hamburg
22763
Deutschland