Forecasting Techniques D
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.
Ana Paula Ferreira Barroso, António Carlos Bárbara Grilo
Weekly - Available soon
Total - 47
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.
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
Estimating trends with differences
Multiplicative decomposition method
Additive decomposition method
Decomposition using regression models
Univariate ARIMA models
Programs where the course is taught: