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Machine Learning for Time-Series with Python
Machine Learning for Time-Series with Python

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

Profile Icon Ben Auffarth
By Ben Auffarth
zł59.99 zł177.99
Book Oct 2021 370 pages 1st Edition
eBook
zł59.99 zł177.99
Print
zł221.99
Subscription
Free Trial
Profile Icon Ben Auffarth
By Ben Auffarth
zł59.99 zł177.99
Book Oct 2021 370 pages 1st Edition
eBook
zł59.99 zł177.99
Print
zł221.99
Subscription
Free Trial
eBook
zł59.99 zł177.99
Print
zł221.99
Subscription
Free Trial

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Machine Learning for Time-Series with Python

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Key benefits

  • 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

Description

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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 29, 2021
Length 370 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781801819626
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Oct 29, 2021
Length 370 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781801819626
Category :

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Table of Contents

15 Chapters
Preface Chevron down icon Chevron up icon
1. Introduction to Time-Series with Python Chevron down icon Chevron up icon
2. Time-Series Analysis with Python Chevron down icon Chevron up icon
3. Preprocessing Time-Series Chevron down icon Chevron up icon
4. Introduction to Machine Learning for Time-Series Chevron down icon Chevron up icon
5. Forecasting with Moving Averages and Autoregressive Models Chevron down icon Chevron up icon
6. Unsupervised Methods for Time-Series Chevron down icon Chevron up icon
7. Machine Learning Models for Time-Series Chevron down icon Chevron up icon
8. Online Learning for Time-Series Chevron down icon Chevron up icon
9. Probabilistic Models for Time-Series Chevron down icon Chevron up icon
10. Deep Learning for Time-Series Chevron down icon Chevron up icon
11. Reinforcement Learning for Time-Series Chevron down icon Chevron up icon
12. Multivariate Forecasting Chevron down icon Chevron up icon
13. Other Books You May Enjoy Chevron down icon Chevron up icon
14. Index Chevron down icon Chevron up icon
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