Introduction to time series data -- Smoothing and decomposing a time series -- Summary statistics of stationary time series -- The algebra of differencing and backshifting -- Stationary stochastic processes -- ARIMA(p,d,q)(P,D,Q)f modeling and forecasting -- Latent process models for time series -- Vector autoregression -- Classical regression with ARMA residuals -- Machine learning methods for time series.
Summary:
"Learn by doing with this guide to classical and contemporary machine learning approaches to time series data analysis. With datasets, commented R programs, case studies and quizzes, this is an essential and accessible resource for undergraduate and graduate students in statistics and data science, and researchers in data-rich disciplines"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.