Applied Forecasting

The Applied Forecasting course is supported on the fundamental contents of Statistics. The programming knowledge provided by the Data Science Programming curricular unit is a plus.

The classes are designed, according to the curriculum plan, to be both theoretical and practical. They are planned and prepared to actively engage students at various moments or throughout the entire class.

In the theoretical part of the lesson, the expository method will be frequently used to introduce concepts, fundamental results, and methods, interspersed with tasks that encourage active participation by all students (interactive lectures). These tasks include posing questions to and by students, orally and/or on a platform, as well as proposing debates/discussions in small groups on certain exposed aspects/topics. 

The practical part will be designed to comprehensively develop the listed skills. This will be achieved through commented exemplification of procedures and/or problem-solving under the guidance/tutoring of the teacher. Autonomous work or work in small groups will be encouraged, progressing towards project-based learning, with the completion of an assignment. There will be a strong interaction between theory and practice, with a central focus on visualizing and dealing with actual scenarios. The computer tool that will be mainly used is Google Colab with Python language.

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.

Data description and forecasting tasks often involve data where the time component cannot be neglected. Thus, in this curricular unit, the focus is on time series data.

Goals 

• Outline a forecasting project involving temporal data and quantitative forecasting methods.
• Critically analyze the data according to the forecast objective set.
• Make the desired forecasts, following good practices for assessing predictive capacity of the models considered.

Skills

• Collect and execute exploratory analysis of temporal data to make forecasts.
• Identify potential problems that interfere with the quality of the data and carry out the due treatment.
• Identify and apply the appropriate forecast quantitative method(s) in specific cases.
• Validate the assumptions and point out the limitations of the methods applied to obtain models.
• Assess the predictive capacity of the models obtained and select the most appropriate one.
• Use a computer tool to support the analysis and prediction process.
• Present and discuss the results obtained in the analysis and prediction process.

1. Time series description and pre-processing
    1.1.  Exploratory analysis
    1.2.  Decomposition
    1.3.  Pre-processing

2. Time series forecasting
    2.1.  Toolkit: concepts, procedures and measures
    2.2.  Exponential smoothing
    2.3.  ARIMA models
    2.4.  Dynamic regression models
    2.5.  Advanced forecasting methods

NAO

Fundamental:

• Hyndman, R.J., Athanasopoulos, G. (2021). Forecasting: Principles and Practice, 3rd edition. OTexts. https://otexts.com/fpp3/
• Nielsen, A. (2019). Practical Time Series Analysis: Prediction with Statistics and Machine Learning. O’Reilly Media.

Complementary:

• Box, G.E., Jenkins, G.M., Reinsel G.C. (2015). Time Series Analysis: Forecasting and Control, 4th edition. Wiley.
• Gilliland, M., Tashman, L., Sglavo, U. (2021). Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning. Wiley.
• Hewamalage, H., Ackermann, K., Bergmeir, C. (2023). Forecast evaluation for data scientists: common pitfalls and best practices. Data Mining and Knowledge Discovery, 37, 788-832. https://doi.org/10.1007/s10618-022-00894-5
• Kolassa, S., Rostami-Tabar, B., Siemsen, E. (2023). Demand Forecasting for Executives and Professionals. CRC Press. https://dfep.netlify.app/
• Joseph, M. (2022). Modern Time Series Forecasting with Python. Packt Publishing.
• Peixeiro, M. (2022). Time Series Forecasting in Python. Manning.
• Petropoulos, F. et al. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38, 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001