Course outline
This unit will introduce you to concepts of data analysis using time series forecasting techniques. You will analyse and interpret the patterns in a set of given data, estimate the future observations of data using the trends identified, and use software for statistical analysis and forecasting.
Lecturer
Fotios Petropoulos is Professor of Management Science at the School of Management of the University of Bath. He serves as an Editor for theInternational Journal of Forecastingand asForesight’s Editor for Forecasting Support Systems. He is interested in research on time series forecasting, judgmental approaches for forecasting, statistical and judgmental model selection and integrated business forecasting processes. Fotios’ research so far has focused on the improvement of forecasting processes and more specifically around two streams. First, he has examined how additional information can be extracted from time series data through time transformation (temporal aggregation) and the use of hierarchies. Second, he has investigated interactions between forecasting and management judgment.
Prerequisites
This course is suitable for students who are interesting in predictive analytics and have a strong background in statistics and economics. Also, basic knowledge of programming is necessary, as the practical part of the course will be delivered using the R statistical software.
Register the course
Please scan this QR code to register. Registration is open till Nov 17 24:00.
Schedule and contents
Lecture |
Time (Beijing time) |
Topic |
Nov 20 Saturday15:00-19:00 Module 1: Time series decomposition, simple time series methods, forecast evaluation |
1 |
15:00-15:45 |
Time series patterns and decomposition |
2 |
16:00-16:45 |
Simple time series forecasting methods |
3 |
17:00-17:45 |
Measures for evaluating forecasts |
Lab 1 |
18:00-18:45 |
Practical demonstration using R |
Nov 21 Sunday15:00-19:00 Module 2 : Exponential smoothing |
4 |
15:00-15:45 |
Forecasting with trend patterns |
5 |
16:00-16:45 |
Forecasting with seasonal patters |
6 |
17:00-17:45 |
Automatic exponential smoothing modelling |
Lab 2 |
18:00-18:45 |
Practical demonstration using R |
Nov 27 Saturday15:00-19:00 Module 3:Forecasting withARIMA |
7 |
15:00-15:45 |
Autoregressive Models |
8 |
16:00-16:45 |
Moving Average Models |
9 |
17:00-17:45 |
ARIMA and Seasonal ARIMA models |
Lab 3 |
18:00-18:45 |
Practical demonstration using R |
Nov 28 Sunday15:00-19:00 Module 4: Combination and aggregation (cross-sectional and temporal) |
10 |
15:00-15:45 |
Forecast Combinations and the Theta method |
11 |
16:00-16:45 |
Forecasting with aggregation using cross-sectional data |
12 |
17:00-17:45 |
Forecasting with aggregation using temporal data |
Lab 4 |
18:00-18:45 |
Practical demonstration using R |
