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.
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