Business Forecasting in R

Develop expertise in business forecasting by combining theoretical foundations with practical applications in R. 

Description

  • Designed for professionals to enhance their forecasting skills, this course combines theory with practical applications using R, focusing on modern, widely used forecasting methods and benchmark models.

Learning objectives

  • Prepare time series data and identify patterns in that data.
  • Appreciate different types of forecasting models.
  • Produce forecasts from forecasting models.
  • Assess the performance of forecasting models using appropriate forecasting evaluation approaches.
  • Perform forecasting tasks in R.

Topics

  • Time series data and decomposition (handling time series data and data exploration with decomposition).
  • Simple methods to forecasting (mean, naïve forecast, simple exponential smoothing).
  • Forecast evaluation (the choice of error metrics and rolling-origin evaluation).
  • Forecasting with univariate models (exponential smoothing and ARIMA).
  • Advanced methods in forecasting (hierarchical forecasting)

Audience

Anyone who are interested in upgrading their forecasting skills in R.

Course format

  • A mixture of lectures and computer-based tutorials, with direct supports from the trainer.
  • Lecture notes, which contain discussion on forecasting topics.
  • Datasets for workshops, which are mostly real-life datasets.

Prices

  • Member £495 + VAT
  • Non-Member £625 + VAT
  • Student Member £370 + VAT
  • For group booking discounts please contact [email protected]

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Meet the tutor

Kandrika Pritularga

Kandrika is a Lecturer in Management Science at Lancaster University. He earned his doctoral degree from Lancaster University, with the title of ‘Mitigating Parameter Uncertainty in Business Forecasting’ and his thesis received an award. His research interests are mainly in statistical forecasting models, with business, healthcare, and tourism application. Specifically, Kandrika is interested in the model estimation and mitigating uncertainty in modelling. Kandrika has developed an estimation procedure in univariate and multivariate exponential smoothing models, and examines hierarchical forecasting in detail.

Kandrika is an active member of forecasting communities nationally and globally, e.g., the Centre for Marketing Analytics and Forecasting and the International Institute of Forecasting. He delivered different types of training for doctoral students and practitioners. Kandrika has also been working with different National Health Service Trusts across England.