Hands-on Time Series Analysis With Python From Basics To Bleeding Edge Techniques

This book explains the concepts of time series from traditional to bleeding-edge techniques with full-fledged examples. The book begins by covering time-series fundamentals and their characteristics, Structure & Components of time series data, preprocessing, and ways of crafting features through data wrangling. Next, it covers traditional time-series techniques such as the smoothing methods ARMA, ARIMA, SARIMA, SARIMAX, VAR, and VARMA using trending frameworks such as Statsmodels and Pmdarima. Further covers how to leverage advanced deep learning-based techniques such as ANN, CNN, RNN, LSTM, GRU, and Autoencoder to solve time-series problems using Tensorflow. It concludes by explaining how to use the popular framework fbprophet for modeling time-series analysis. After completion of the book, the reader will have a thorough knowledge of concepts and techniques to solve time-series problems. All the code presented in this book is available in Jupyter Notebooks; this allows readers to do hands-on experiments and enhance them in exciting ways.