Time Series

The Time Series course is supported on the fundamental contents of Statistics. The programming knowledge provided by the programming courses 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.

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.

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

Outcomes:

1. Explain the key concepts of time series analysis and forecasting.
2. Conduct exploratory analysis and the respective processing of time series data, taking into account the defined analysis and/or forecasting objectives.
3. Select, apply and compare suitable forecasting methods for time series, critically evaluating and following best practices in assessing the predictive performance of the models obtained.
4. Design, implement and communicate a complete forecasting solution for time series, justifying the methodological decisions taken.

Skills:

1. Acquire, integrate and structure time series obtained from files, application programming interfaces (APIs) or databases, ensuring temporal integrity throughout the process.
2. Describe time series through exploratory analysis combining visualisation, descriptive statistics and measures of temporal dependence, highlighting regularities, structural breaks and anomalies characteristic of this type of data.
3. Identify and address potential problems affecting data quality (missing values, outliers and structural breaks, among others) and carry out the appropriate treatment.
4. Construct new variables from time series (lags, differences, aggregations, exogenous indicators) that enhance the explanatory and/or predictive power of the models.
5. Identify and implement suitable quantitative forecasting methods, according to the characteristics of the problem under study.
6. Verify the assumptions of the estimated models, highlight their limitations and discuss the implications for practical use.
7. Evaluate the predictive capacity of the models obtained and select the most appropriate alternative, following best practices.
8. Use and automate computer tools to support all stages of the time series analysis and forecasting process.
9. Document and communicate, both orally and in writing, the process followed (including choices and technical justifications), the results obtained and the final recommendations.

1. Time series analysis
     1.1. Exploratory analysis
     1.2. Handling missing values
     1.3. Anomaly detection
     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

In accordance with the "Regulamento Académico do 1.º Ciclo de Estudos do IPC", the student will be assessed by the periodic assessment system, which includes a project with its oral defense and a an exam. Both components are compulsory, thus assessment by exam alone is not possible.

The exam is an individual written examination, graded out of 6 points with a minimum pass mark of 2.0 points, and is held on a date set in the official examination schedule. During the exam, students may consult two sheets of paper (double-sided) containing a summary of the subject matter prepared by the student. Alternatively, the assessment exam may consist of two individual written tests, each graded out of 3 points with a minimum pass mark of 1.0 points. These tests are held during the teaching period without the need for prior booking and may cover any subject taught and recorded up to the class preceding the test. In each test, students may consult one sheet of paper (double-sided) containing a summary of the subject matter prepared by the student.

The project and its oral defence are graded out of 14 points and require a minimum pass mark of 7.0 points. The announcement and submission are carried out through the InforEstudante|Nonio platform. The rules governing the use of generative artificial intelligence in the project will be clearly stated at the time of its announcement. Detection of plagiarism or improper use of generative artificial intelligence will result in the annulment of the project. The nature of the project depends on the period in which it is submitted:

• If submitted for assessment during the teaching period, it is a group project, developed in several parts, with submission deadlines and oral defences scheduled during that period.
• If submitted for assessment in an examination period, it is an individual project, to be submitted up to three working days before the date of the examination for which the student is registered, with the project presentation taking place on the same day as the examination.

The final grade (CF) is the sum of the assessment exam grade (CP) and the project grade (CT): CF = CP + CT. Approval is granted only if the minimum grade requirements for each component are met and the final grade, rounded to the nearest whole number, is equal to or greater than 10 points.

Final notes:

• A student who, in a given examination period, obtains at least the minimum grade in only one of the two assessment components will retain the grade for that component in subsequent examination periods within the same academic year until approval is obtained.
• If the minimum grade is not achieved in the first test, the student must take the full exam.
• Grade improvement requires the completion of a new exam and a new individual project, both in the examination period in which the student is registered for grade improvement.

NAO