Course manual 2020/2021

Course content

From the beginning, students will learn how to analyze and forecast the dynamics of large-scale real-complex time-series data, from social or technological systems and forecast. Students will learn how to describe time-series prediction methods and the applications of these methods to different types of data in various contexts. Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts, and often it is necessary to provide distributional rather than point forecasts. As such, an introduction to methods for probabilistic forecasting will also be provided. Time series modeling techniques will be considered with reference to their use in forecasting where suitable. While linear models will be examined in some detail, extensions to non-linear models using machine learning approaches will also be considered for real-world problems. In the course, students will learn the theoretical concepts during the lectures as exploring different problem domains, build up their skills by practicing on assignments, and finally demonstrate their knowledge and skills by participating in a Kaggle competition or project using a modeling environment.

Study materials

Literature

Syllabus

A syllabus containing articles and chapters will be made available at the beginning of the course. The syllabus/course will cover parts of the books proposed in the literature.

Software

Python, R.

Objectives

Explain to what extent results derived from forecasts are useful to the problem at stake.
Review and interpret modeling and forecasting results critically.
Discuss the potential setup of forecasting models.
Analyze the visualization of uncertainty attaching to forecasts, and how it relates and differentiates from point forecasts.
Select the most appropriate forecasting method and devise time series prediction models suitable for solving real-problem.

Teaching methods

Lecture
Computer lab session/practical training
Self-study
Seminar

Learning activities

Activity	Hours
Hoorcollege	28
Laptopcollege	28
Tentamen	2
Self study	110
Total	168	(6 EC x 28 uur)

Attendance

In TER part B of this programme no requirements regarding attendance are mentioned.

Assessment

Item and weight	Details	Remarks
Final grade		FP≥ 5 0 then G=(FP+(A 1 +A2)/2)2, otherwise G= FP
1 (33%) Assignment 1	Must be ≥ 5.5
1 (33%) Assignment 2	Must be ≥ 5.5
1 (33%) Final Project	Must be ≥ 5.5

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer	Onderwerpen	Studiestof
1
2
3
4
5
6
7
8

Owner	Master Information Studies
Coordinator	dr. ing. C.M. Rodriguez Rivero
Part of	Master Information Studies, track Data Science, year 1 Master Information Studies, track Information Systems, year 1

Applied Forecasting in Complex Systems