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


Weekly - 4

Total - 56

Teaching language



Available soon


Shmueli, G. (2018) Practical Time Series Forecasting. A Hands-on Guide. 3rd ed., Axelrod Schnall Publishers.

Keating B. e Wilson J.H. (2019) Forecasting and Predictive Analytics. Boston, McGraw Hill Education.

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.E.P.,

Box G.E.P., Jenkins G.M. e Reinsel G. C. (1994) Time Series Analysis, Forecasting and Control, 3rd ed., Englewood Cliffs, Prentice-Hall.

Teaching method

In lectures the concepts, methods and models are discussed. 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 assessment  includes the following components with the respective weighting in the final grade:

a) Theoretical-Practical Assessment (50%) - Individual

2 tests (T1 and T2). 

b) Laboratory or Project Assessment (50%) - In group

4 group projects (Trbs)

Final grade =  0.25 T1 + 0.25 T2 + 0.50 Trbs average

Dates:  T1 - 18/oct   e   T2 - 29/nov  

The score for each component of the assessment will be rounded to two decimal places.

The final grade and the average of T1 and T2 must be at least 9.50 (out of 20) for the student to be approved.

If the average of T1 and T2 is less than 9.50, but the average of group projects/assignments is greater than 9.50, the student can take the final exam (Exame de Recurso), which replaces only T1 and T2.

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. Special forecasting considerations.