{"id":1007,"date":"2016-04-17T12:48:39","date_gmt":"2016-04-17T12:48:39","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1007"},"modified":"2016-04-17T12:49:19","modified_gmt":"2016-04-17T12:49:19","slug":"workshop-on-forecasting-with-r","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2016\/04\/17\/workshop-on-forecasting-with-r\/","title":{"rendered":"Workshop on `Forecasting with R’"},"content":{"rendered":"

Fotios Petropoulos<\/a> and I will be giving a workshop on how to produce forecasts using R. The focus will be on business forecasting applications and the workshop is part of the workshop series that runs as part of the 36th International Symposium on Forecasting<\/a> on the 19th of June 2016.<\/p>\n

This workshop will provide a full-day hands-on demonstration of R statistical software as a forecasting tool. Assuming basic knowledge over the basic forecasting methods (exponential smoothing, ARIMA, single and multiple regression models) we will demonstrate how to use R to produce forecasts with a wide range of methods and models. We will follow a step-by-step approach from data input, modelling up to forecast evaluation and export of results. On top of basic methods, we will show how to easily apply in practice more sophisticated forecasting, such as intermittent demand methods and cross-sectional and temporal hierarchical forecasting. We will go through the basic functions of major forecasting related R packages (such as forecast<\/em>, tsintermittent<\/em> and other specialised packages). Using hands-on exercises and real-world data, participants will learn to model time series, produce forecasts, visualise and evaluate results.<\/p>\n

The workshop is tailored for both beginners and intermediate R users and it will be structured in five sessions:<\/p>\n

    \n
  1. Introduction to R and general use functions for forecasting.<\/li>\n
  2. Forecasting for fast demand (Exponential smoothing and ARIMA)<\/li>\n
  3. Forecasting with causal methods (Regression and variable selection)<\/li>\n
  4. Forecasting for intermittent demand (Croston, SBA and classification schemes)<\/li>\n
  5. Advanced methods in forecasting (Hierarchical forecasting, temporal aggregation and MAPA, …)<\/li>\n<\/ol>\n

    More details how to participate to the workshop are provided at the ISF<\/a> website.<\/p>\n

    Related Posts<\/H3>