Degree in Business Intelligence and Data Analytics

Lead the transformation of companies through the use and analysis of data.

Forecasting

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Description
This subject addresses an essential aspect of data analysis, such as temporary series. Many of the business problems that students will face during their professional career require this type of analysis, since much of the information necessary for decision making is structured in this way. The main objective of this course is to provide students with a working methodology and a solid knowledge for the use of times series analysis and forecasting models and techniques in business and economics.
Type Subject
Tercer - Obligatoria
Semester
First
Course
2
Credits
6.00

Titular Professors

Hugo Zarco Jasso
Membre

Professors

Hugo Zarco Jasso
Previous Knowledge
Objectives

The Learning Outcomes of this subject are:
RA.01 Identify patterns in correlated data - trends and seasonal variation
RA.02 Understand and modeling time-series data
RA.03 Use forecasting techniques to predict short-term trends from previous patterns
RA.04 Identify the tools that need to be used in time-series and forecasting situations

Contents

1. Introduction and course overview
2. Time series graphics and key components of time series
3. The forecasters' toolbox and judgmental forecasts
4. Time series regressions models
5. Time series decomposition
6. Exponential smoothing and trend methods
7. Autoregressive and moving average models
8. Simple forecasting methods
9. Advanced forecasting methods. New trends

Methodology

There will be two weekly sessions, four hours in total. This will be complemented by practice problems and homework.
Requirements and expectations:
- Attend class AND participate. This course is practical, and only effective if everyone participates actively. Simply attending classes does NOT count as participation.
- Be punctual. Come to class on time. If you are late, this might count as an absence.
- Be prepared for every class. We will have short exercises from time to time, and these will NOT be announced.
- Demonstrate academic integrity in all of your work. If you are caught cheating in any form on exams, be prepared to receive a failing final grade for the course, and face the consequences according to the Plagiarism Rules and Guidelines.
- Follow the mobile phones & laptops policy. Mobile phones are off limits during class: no calls - no text messaging, unless we are using them for an activity.
- Revision date. After each evaluation (midterm/final exams) and once grades have been posted, there will be a time slot for revision of the exam. There will be no revision of exams after this date.

Evaluation

Exercises and class participation: 20%
Midterm exam: 25%
Team project: 25%
Final Exam: 30%

Retake Exam: The retake will be a written exam, and the maximum grade for the retake is 6.

Evaluation Criteria
Basic Bibliography

Shumway, R. and Stoffer, D. (2017). Time Series Analysis and Its Applications. With R examples. Springer.

Additional Material