This course is an intuitive introduction to forecasting and analysis of time-series data. We will review a range of standard forecasting methods, including ARIMA and exponential smoothing, along with standard means of measuring forecast error and benchmarking with naive forecasts, and standard pre-processing/de-trending methods such as differencing and missing value imputation. Other topics will include trend/seasonality/noise decomposition, autocorrelation, visualisation of time series, and forecasting with uncertainty.
Who should attend?
This course is suitable for all managers, executives and specialists who want to make better decisions under uncertainty. It requires no specialised statistical knowledge, nor knowledge of R.
The course will be instructed by Presciient Director, Dr Eugene Dubossarsky. Eugene is a founder and Fellow of the Institute of Analytics Professionals of Australia (IAPA); Director, University of New South Wales School of Mathematics and Statistics Industry Advisory Board; and a recognised industry leader in Business Analytics. Eugene is an experienced, professional data miner of 20 years’ experience programming in R and its parent language, S.
Attendees are recommended to have completed Presciient’s Introduction to R two-day course, or equivalent.