ETC3550 · Applied forecasting

Overview

Business and economic data are collected over time against a backdrop of a changing environment. This unit introduces data wrangling and exploratory data analysis for such temporal context data. You will learn how to analyse, model and forecast time series data in diverse applications. Methods to be covered include the decomposition of time series, exponential smoothing methods, ARIMA modelling, and regression with auto-correlated disturbances. You will enhance your computational skills with exercises using R.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. understand common statistical methods used in business and economic forecasting
2. develop computer skills for forecasting business and economics time series data
3. provide insights into the problems of implementing and operating large scale forecasting systems.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.