ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01)
https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/ Use discount code 'JAN25' to make the most of our Jan sale worth 20% off all courses Instructor - Dr. Rafael De Andrade Moral 27th Jan - 5th Feb 2025 Please feel free to share! In this six-day course (Approx. 35 hours), we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas, such as finance, meteorology, ecology, public policy, and health. We start by introducing the concepts of time series and stationarity, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then, we will introduce benchmark forecasting methods, namely the naïve (or random walk) method, mean, drift, and seasonal naïve methods. After that, we will present different exponential smoothing methods (simple, Holt’s linear method, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally, we will cover Bayesian implementations of time series models and introduce extended models, such as ARCH, GARCH and stochastic volatility models, as well as Brownian motion and Ornstein-Uhlenbeck processes. Please email oliverhoo...@prstatistics.com with any questions. Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
