| Summary: | 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 its characteristics, the structure of time series data, pre-processing, and ways of crafting the features through data wrangling. Next, it covers the traditional time series techniques like Smoothing methods, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA using trending framework like StatsModels, pmdarima. Further, Book explains the building classification models using sktime, and covers how to leverage advance deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It finally concludes by explaining the popular framework fbprophet for modeling time series analysis. After completion of the book, the reader will have a good understanding of working with different techniques of time series methods. All the codes presented in this notebook are available in Jupyter notebooks, which allows readers to do hands-on and enhance them in exciting ways. What You'll Learn Explains basics to advanced concepts of time series How to design, develop, train, and validate time-series methodologies What are smoothing, ARMA, ARIMA, SARIMA, SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. Univariate and multivariate problem solving using fbprophet
|
|---|