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Table Of Contents
Machine Learning for Time-Series with Python - Second Edition
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Machine Learning for Time-Series with Python
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Overview of this book
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.
Table of Contents (3 chapters)
Machine Learning for Time-Series with Python, Second Edition: Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Free Chapter
Introduction
Dealing with Time Series in Python
Time series problems are very important in the industry and academia. Many commercial applications use time series data, such as stock market analysis and predicting consumer behavior in retail. Time series data is used extensively in weather forecasting, stock market prediction, demand forecasting, and many more applications.The landscape in machine learning for time series has been changing and many different libraries and algorithms are out there to deal with time series. Some popular methods for time series include ARIMA, LSTM, Prophet, and SARIMA. This book is trying to help choose the right tools and approaches for specific situations.In this chapter, we'll discuss time series and the typical set of problems associated with them in more detail. After reading this chapter, you should become familiar with the following:
