Ctrl+K

Resources and acknowledgments

Contents

Resources and acknowledgments#

The following resources served as inspiration and provided very useful content to write some of the sections of the book.

  • Intel course on time series [Link].

  • Collection of links for books, articles, frameworks, etc… on time series [Link].

  • A “semi-auto” way to determine parameters for SARIMA model [Link].

  • Book on Time Series analysis (University of California) [Link].

  • Lecture on stats models, ESN, and state-space reconstruct [Link].

  • Time series classification and clustering with Reservoir Computing Link

  • Medium article on Fourier transform [Link]

  • Neptune blog, inspired by the Intel course [Link].

  • Cheat-sheet TS models in Python [Link].

  • Time series analysis with Python [Link].

  • An Introduction to Kalman Filter [Link].

  • Python library to extract static features from time series data [Link].

  • Basic Concepts in Nonlinear Dynamics and Chaos [Link].

  • IPython Cookbook, Second Edition (2018) [Link].

  • Introduction to Taken’s Embedding [link].

  • An introduction to Dynamic Time Warping [link].

  • An intuitive approach to DTW — Dynamic Time Warping [link].

Acknowledgments#

Thanks to:

  • Simone Scardapane for spotting many typos, giving feedback, and suggestions.

  • Jonas Berg Hansen for giving feedback on the exercises.

Contents