Machine Learning for Time Series with Python - Second Edition : Python to forecast, predict, and monitor time series with modern ML and foundation models at scale. Dieser Artikel ist nicht verfügbar.Machine Learning for Time Series with Python - Second Edition : Python to forecast, predict, and monitor time series with modern ML and foundation models at scale

Get better insights from time-series data and become proficient in building models with real-world data

Key Features

• Explore popular and state-of-the-art machine learning methods, including the latest online and deep learning algorithms
• Learn to increase the accuracy of your predictions by matching the right model to your problem
• Master time series in Python via real-world case studies on operations management, digital marketing, finance, and healthcare

Book Description

The Python time-series ecosystem is a huge and challenging topic to tackle, especially for time series since there are so many new libraries and models. Machine Learning for Time Series, Second Edition, aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and helping you build better predictive systems.

This fully updated second edition starts by re-introducing the basics of time series and then helps you get to grips with traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will gain a deeper understanding of loading time-series datasets from any source and a variety of models, such as deep learning recurrent neural networks, causal convolutional network models, and gradient boosting with feature engineering. This book will also help you choose the right model for the right problem by explaining the theory behind several useful models. New updates include a chapter on forecasting and extracting signals on financial markets and case studies with relevant examples from operations management, digital marketing, and healthcare.

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

• Visualize time series data with ease
• Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
• Get to grips with classical time series models such as ARMA, ARIMA, and more
• Understand modern time series methods including the latest deep learning and gradient boosting methods
• Choose the right method to solve time-series problems
• Become familiar with libraries such as Prophet, sktime, statsmodels, XGBoost, and TensorFlow
• Understand both the advantages and disadvantages of common models
• Evaluate high-performance forecasting solutions

Who This Book Is For

This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.

Table of Contents

1. Introduction
2. Dealing with Time Series in Python
3. Time Series Analysis
4. Forecasting with moving averages and autoregressive models
5. Machine Learning for Time Series
6. Unsupervised machine learning
7. Drift and adaptive models
8. Reinforcement learning
9. Event time prediction
10. Probabilistic models and confidence
11. Deep Learning
12. Multivariate forecasting

Ben Auffarth is a full-stack data scientist who has >15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience from one of Europe's top engineering universities, 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 of thousands of transactions per day, and trained neural networks on millions of text documents. In his work, he often notices a lack of appreciation for the importance of time-related factors, a deficit he wanted to address in this book. He co-founded and is the former president of Data Science Speakers, London.