Book Image

Machine Learning for Time-Series with Python

By : Ben Auffarth
Book Image

Machine Learning for Time-Series with Python

By: Ben Auffarth

Overview of this book

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.
Table of Contents (15 chapters)
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In this chapter, we introduced TSA as the process of extracting summary and other statistical information from time-series. We broke this process down into understanding the variables, uncovering relationships between variables, and identifying trend and seasonality.

We introduced datetime and pandas, the libraries sine qua non in TSA, and their functionalities for time-series; for example, resampling. Throughout the chapter, we listed and defined many summary statistics including mean, standard deviation, median, SE, confidence interval, Pearson correlation, and covariance.

We also talked about the concepts of seasonality, cyclic variation, and stationarity. We discussed why stationarity is important, and how to test for stationarity.

We also showed plotting functionality with Matplotlib and Seaborn, and how to generate different plots such as run charts, temporal line charts, correlation heatmaps, histograms, scatter plots, autocorrelation plots, and periodograms. In the practical example, we used an autocorrelation plot, which shows the correlation between different time steps, and the periodogram, which visualizes the power spectral density.