Time Series

The classes are, according to what is determined in the curricular plan, both theoretical and practical. In the theoretical part of the lesson,
the expository method will be used predominantly to introduce concepts, fundamental results and methodologies. The practical sessions will
be aimed at exemplifying procedures and problem solving under the guidance of the teacher, but encouraging autonomous work or in small
groups with the support of a computer tool (predominantly, Google Colab with Python or R language). A strong interaction between theory
and practice will prevail, giving, as much as possible, a central role to the visualization and treatment of specific situations.

Data analysis and forecasting tasks, in the most diverse scientific fields, often involve data where the time component cannot be neglected.
Thus, in this curricular unit, the focus is on time series data. It is intended that the student is able to:
– carry out the exploratory analysis adjusted to the time series of interest;
– transform and/or extract new variables from the given data;
– identify potential problems that interfere with the quality of the data and carry out the due treatment;
– recognize different classes of forecasting methods for time series;
– identify and apply the appropriate forecast method(s) in specific cases;
– know the assumptions and limitations of the methods;
– know how to validate the obtained models and evaluate their predictive capacity.
At the same time, the student must be able to perform the tasks in an efficient manner using the appropriate computer(s) tool.

1. Time series analysis
1.1. Exploratory analysis
1.2. Handling missing values
1.3. Anomaly detection in time series
1.4. Feature transformation and extraction
2. Time series forecasting
2.1. Fundamental concepts
2.2. Diagnostics evaluation
2.3. Forecasting accuracy evaluation
2.4. Benchmark forecasting methods
2.5. Statistical forecasting methods
2.6. Machine learning forecasting methods

NAO

– Atwan, T.A. (2022). Time Series Analysis with Python Cookbook. Packt.
– Auffarth, B. (2021). Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine
learning methods. Packt.
– Box, G.E., Jenkins, G.M., Reinsel G.C., Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, 5th edition. Wiley.
– Caiado, J. (2016). Métodos de Previsão em Gestão com aplicações em Excel, 2.ª Edição (revista e aumentada). Edições Sílabo.
– Gilliland, M., Tashman, L., Sglavo, U. (2021). Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning.
Wiley.
– Hyndman, R.J., Athanasopoulos, G. (2021). Forecasting: Principles and Practice, 3rd edition. OTexts.
– Nielsen, A. (2019). Practical Time Series Analysis: Prediction with Statistics and Machine Learning. O’Reilly Media.
– Peixoto, M. (2022). Time Series Forecasting in Python. Manning.