Forecasting Techniques


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.

General characterization





Responsible teacher

Ana Paula Ferreira Barroso, António Carlos Bárbara Grilo


Weekly - 4

Total - 61

Teaching language



Available soon


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 (3 in group, TGr1, TGr2, TGr3), with a weighting of 50 and 50% in the final grade, respectively.

Final Grade =  0.25 T1 + 0.25 T2 0.15 TGr1 + 0.10 TGr2 + 0.25 TGr3

To be exempted from the final exam, the student must assure a mark equal or above 9.5 on the average of closed-book tests.

The student is excluded from the final exam if he / she is not present in at least 9 lectures and 9 laboratory sessions, and the grade of TGr1, TGr2 and TGr3 does not exceed 9.5 values.

Subject matter

  1. Planning and forecasting. Types of forecasting. Statistical fundamentals for forecasting. Quantitative and qualitative forecasting
  2. Exploring time series data patterns. Adjusting outliers in time series with and without seasonal pattern. 
  3. Fitting versus forecasting: absolute and relative measures of error. Autocorrelation and ACF (k)
  4. Univariate methods to model time series without trend or seasonality: simple smoothing methods: simple moving averages, weighted moving averages, exponential smoothing
  5. Univariate methods to model time series with trend (no seasonality). Linear regression models. Estimating trends with differences. Brown’s model. Holt’s model
  6. Univariate methods to model time series with trend  and seasonality. Holt-Winters’ model. Multiplicative decomposition method. Additive decomposition method. Decomposition using regression models
  7. Univariate ARIMA models. ARIMA applications


Programs where the course is taught: