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ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01)

Oliver Hooker 
ONLINE COURSE ? Time Series Analysis and Forecasting using R and Rstudio
(TSAF01)

We still have a few places available for our upcoming course on time series
and forecasting in a couple of weeks time.

https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/

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 na?ve (or random walk) method,
mean, drift, and seasonal na?ve 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 oliverhooker at prstatistics.com with any questions.

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