Time Series 2023

Silverkite, Additive and Multiplicative seasonality, Univariate and Multavariate imputation, Statsmodels, and so on

Learn how to forecast cryptocurrency market with Prophet model, time series decomposition, Random Forest, and XGBoost

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail.

The editors of this book are happy to provide the specialized reader community with this book as a modest contribution to this rapidly developing domain.