Follow on to Forecasting : ARIMA modelling for forecasting                 

This course is a natural follow on for delegates who have completed the Foundation course in forecasting and now wish to develop their toolbox further with Autoregressive Integrated Moving Average (ARIMA). 

Description

The course is designed for those who have a basic knowledge of forecasting (e.g., smoothing and regression) who wish to take both diagnostic and time series forecasting approaches to the next level.

Learning objectives

  • The underlying assumptions of ARIMA models
  • The main characteristics of autoregressive and moving average models as used in ARIMA
  • The process of the Box-Jenkins methodology for identifying suitable ARIMA models
  • How to apply the Box Jenkins methodology to both non-seasonal and seasonal data sets
  • To understand when ARIMA models may be appropriate in practice

Topics

  • Underlying principles and useful tools: 
  • Stationarity and white noise 
  • Autocorrelation 
  • Differencing 
  • The suite of models used in ARIMA: 
  • Models for stationary processes: 
  • Autoregressive: AR(p) 
  • Moving average: MA(q) 
  • Mixed autoregressive moving average: ARMA(p,q) 
  • Models for non-stationary processes 
  • Autoregressive integrated moving average: ARIMA(p,d,q) 
  • Seasonal ARIMA(p,d,q)(P,D,Q)s models 
  • Box Jenkins Methodology 
  • Identifying, fitting, and checking models

Audience

The course is designed for those who undertake time series forecasting. Ideally, delegates will have a working knowledge of:

  • Correlation, confidence intervals, sampling 
  • Trend and seasonality 
  • Error measurement including backfit vs forecast error 
  • Short/medium term forecasting methods e.g., smoothing and regression 

Course format

  • PowerPoint presentation to introduce the topics
  • Practical sessions using Minitab software and the Microsoft Excel analysis tool pack
  • Group discussion/work to explore the topics in more detail
  • Bring questions from your own work to embed your learning
  • Supporting resource pack available to use following the course

Related courses

  • Statistical Methods in OR: Multivariate Statistics 
  • Optimisation and (Meta-) Heuristics 
  • Data Envelopment Analysis

Want to run this course in-house? Enquire about running this course in-house

Meet the tutor

Estelle Shale

Estelle has been teaching Management Science and Operational Research since 1996. She lectures, tutors and supervises at all levels from undergraduate to Ph.D.

Having spent 15 years full-time at Warwick, she now works freelance and has recently provided both face-to-face and online teaching and programme design and development for Warwick, Aston, Loughborough and Coventry Universities.

Her consultancy experience includes working with Allied Domecq, Ansells, Network Rail, Volvo, Avery Weigh-Tronix and Potterton. Her research and publications focus on Forecasting (particularly inventory control) and performance assessment (especially Data Envelopment Analysis).

She has also published on teaching pedagogy and has an ongoing interest in developing blended learning approaches and the integration of new technology into traditional delivery mechanisms.