Faculdade de Ciências e Tecnologia

Forecasting Techniques

Code

7201

Academic unit

Faculdade de Ciências e Tecnologia

Department

Departamento de Engenharia Mecânica e Industrial

Credits

6.0

Teacher in charge

Ana Paula Ferreira Barroso, Virgílio António Cruz Machado

Weekly hours

6

Total hours

56

Teaching language

Português

Objectives

The course aims to provide students with knowledge on the application of forecasting methodologies, models and techniques, mainly to support management decision making. This course has a strong practical component of formulation, modeling and solving problems in laboratory.

At the end the course it is intended that students have acquired the skills necessary to understand how the application of forecasting techniques contributes positively on the effectiveness and efficiency on management of both supply chain and organization; have an overview of a comprehensive set of forecasting techniques and a perception of the strengths and weaknesses of each technique, and also develop skills that allow them to select  the forecasting models more suited to the specificities of both data modeling and the goal that should be achieved by forecast.

Additionally it is intended that students will be able to identify relevant aspects with regard to the forecast process, data considerations, model selection, and forecast implementation in large scale problems. It is intended also that students are able to develop a critical sense regarding the importance of demand forecasting in supply chain management, production management, marketing and finance.

Subject matter

Planning and forecasting
Types of forecasting
Exploring time series data patterns
Quantitative and qualitative forecasting
Statistical fundamentals for forecasting
Adjusting outliers in time series with and without seasonal pattern
Fitting versus forecasting: absolute and relative measures of error
Autocorrelation and ACF (k)
Univariate methods to model time series without trend or seasonality: simple smoothing methods: simple moving averages, weighted moving averages, exponential smoothing
Univariate methods to model time series with trend or seasonality
Regression models
Estimating trends with differences
Brown’s model
Holt’s model
Winters’ model
Multiplicative decomposition method
Additive decomposition method
Decomposition using regression models
Univariate ARIMA models
ARIMA applications

Bibliography

Hanke J. E. e Wichern D. W. (2009) Business Forecasting. Pearson International Edition.
Wilson J.H., Keating B. e Galt J. (2009) Business Forecasting with ForecastX. McGraw Hill.
Hoshmand A. R. (2010) Business Forecasting. A practical approach. Routledge, Taylor & Francis Group.
DeLurgio S. A. (1998) Forecasting Principles and Applications. Irwin McGraw-Hill.
Box G.E.P., Jenkins G.M. e Reinsel G. C. (1994) Time Series Analysis, Forecasting and Control, 3th ed., Englewood Cliffs, Prentice-Hall.

Teaching method

In lectures the expositive method is adopted to present concepts, methods and models. Oral questions are frequently made for prerequisite control, knowledge assessment and stimulate students’ participation.

In laboratory sessions the experimental method is adopted. Active methods are used. Students are challenged with multifaceted problems which should be solved in team. Also, case studies are analyzed and discussed in class. 

Evaluation method

The course grading is based on closed-book tests (T1 and T2) and projects (Trbs, 3 in group), with a weighting of 50 and 50% in the final grade, respectively.

Final Grade = 0,25 (T1 + T2) + 0,5 (Trbs)

To be exempted from the final exam, the student must assure a mark equal or above 9,5 in the average of closed-book tests.

The student is excluded from final exam if not present in at least 9 lectures and 9 laboratory sessions.

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