Forecasting Methods

The Forecasting Methods course is supported by the transversal contents of Mathematics, Statistics and Computer Applications curricular units of LCA, LCGP, LGE and LMNI.

The classes are, according to what is determined in the curricular plan, both theoretical and practical, planned and prepared to have an active student involvement in various moments or in the entire class.

In the theoretical part of the lesson, the expository method will be often used to introduce concepts, fundamental results and methodologies, interspersed with methods that encourage a more active participation of the students, namely, with the posing of questions to and by students, orally and /or on a platform, and also with the proposal of debate/discussion in small groups on some exposed aspect/topic.

The practical part will be mainly supported by a computer tool (Excel), with a strong interaction between theory and practice, giving, as much as possible, a central role to visualization and treatment of actual real scenarios. It is designed for the full development of the listed skills, through commented examplification of procedures and/or problem solving under the guidance/tutoring of the teacher, with autonomous or in small groups work being encouraged, evolving towards project-based learning, with the carrying out the group work that goes throuth the entire prediction process.

It is assumed that the studend attends classes regularly and is available for his/her involvement to continue beyond classes, with the beginning or completion of tasks agreed upon in class.

All support materials are available on the InforEstudande|Nonio platform, using, in addition, the Microsoft Office 365 Teams platform, both with institutional access provided by Coimbra Business School|ISCAC.

Objectives

Forecasting is an unavoidable component of organizational management, since it is essential for effective and efficient planning and control, serving as a support in decision making. Forecasting also has a place in audit procedures, where it is necessary in forming expectations regarding the performance of the audited organization. Thus, the course of Forecasting Methods is naturally included in the LCA, LCGP, LGE and LMNI courses, which are intended to train professionals in management or with skills to provide management advice or auditing.

Therefore, this UC aims to provide students with knowledge and fundamental technical skills to analyze and predict temporal data from historical records, as well as sectional and temporal data from complementary information.

 Competencies

It is intended that the student be able to:

• recognize situations that can benefit from quantitative forecasting;
• collect and analyze data to make predictions;
• identify and apply the appropriate quantitative forecasting methods to specific cases;
• validate the assumptions and point out the limitations of the methods applied to obtain models;
• evaluate the predictive capacity of the models obtained and select the most appropriate one;
• use a computer tool, specifically Excel, to support the analysis and prediction process;
• present and discuss the results obtained in the analysis and prediction process.

1. Introduction to quantitative forecasting methods
   1.1. Framing: qualitative methods versus quantitative methods; causal and non-causal quantitative methods
   1.2. General considerations about data
2. Traditional time series decomposition and forecasting methods
   2.1. Classical decomposition
   2.2. Elementary forecasting methods
   2.3. Exponential smoothing
3. Simple and multiple regression
   3.1. Model, extensions and assumptions
   3.2. Estimation
   3.3. Inference
   3.4. Prediction

NAO

Main:

• Albright, S.C., Winston, W.L. (2019). Business Analytics: Data Analysis and Decision Making, 7th Edition. Cengage Learning.
• Caiado, J. (2016). Métodos de Previsão em Gestão com aplicações em Excel, 2.ª Edição (revista e aumentada). Edições Sílabo.
• Hyndman, R.J., Athanasopoulos, G. (2021). Forecasting: principles and practice, 3rd Edition. OTexts (online, open-access textbooks). https://www.otexts.org/fpp3/
• Murteira, B., Ribeiro, C.S., Silva, J.A., Pimenta, C., Pimenta, F. (2015). Introdução à Estatística, 3.º Edição. Escolar Editora.
• Slides e folhas de exercícios disponibilizadas no InforEstudante/NONIO.

Complementary:

• Curto, J.D. (2019). Potenciar os negócios? A Estatística dá uma ajuda! (Muitas aplicações em Excel e poucas fórmulas…), 3.ª Edição. Edição do Autor.
• Gee, S. (2014). Fraud and Fraud Detection: A Data Analytics Approach. Wiley.
• Oliveira, M.M., Santos, L.D., Fortuna, N. (2018). Econometria, 2.ª Edição. Escolar Editora.
• Richardson, V., Teeter, R., Terrel, K. (2019). Data Analytics for Accounting. McGraw-Hill Education.
• Wooldridge, J.M. (2019). Introductory Econometrics: A Modern Approach, 7th Edition. Cengage Learning.