The aim of studying and bibliography

The main aim of study: To familiarize students with the basic mathematical methods and tools used in the analysis and forecasting of time series.

The general information about the module: The module consists of 15 hours of lectures and 15 hours of laboratories. It ends with pass (without an exam).

Bibliography required to complete the module

Bibliography used during lectures

1	P. Hydzik, M. Sobolewski	Komputerowa analiza danych społeczno-gospodarczych	Oficyna Wydawnicza Politechniki Rzeszowskiej, Rzeszów.	2009
2	M.B. Priestley	Spectral analysis and time series. Volume 1: univariate series. Volume 2 : multivariate series, prediction and control	Elsevier: Academic Press.	2004

Bibliography used during classes/laboratories/others

1

A. Zagdański, A Suchwałko

Analiza i programowanie szeregów czasowych. Praktyczne wprowadzenie na podstawie środowiska R

PWN, Warszawa.

2016

Bibliography to self-study

1

G.E.P. Box, G.M. Jenkins

Analiza szeregów czasowych. Prognozowanie i sterowanie

PWN, Warszawa.

1983

Basic requirements in category knowledge/skills/social competences

Formal requirements: Tthe completion of the module Programming in R. The student satisfies the formal requirements set out in the study regulations.

Basic requirements in category knowledge: Knowledge of the basics of mathematical analysis and probability theory.

Basic requirements in category skills: Knowledge of the R environment at the basic level.

Basic requirements in category social competences: Willingness to take objectively justified mathematical operations in order to solve the task .

Module outcomes

MEK	The student who completed the module	Types of classes / teaching methods leading to achieving a given outcome of teaching	Methods of verifying every mentioned outcome of teaching	Relationships with KEK	Relationships with PRK
01	knows the concept of a time series and can make a descriptive analysis of a time series using selected indexes.	lecture, laboratory	project	K_W01+ K_U02+ K_U03+ K_K01+	P6S_KK P6S_UW P6S_WG
02	knows how to decompose a time series, designate a classic trend model using a spreadsheet and basic statistical packages (eg in R).	lecture, laboratory	project	K_W01+ K_W02+ K_U02+ K_U03+ K_U05+ K_U07+ K_U18+ K_K01+ K_K02+ K_K05+	P6S_KK P6S_KO P6S_UW P6S_WG
03	can prepare simple ARIMA models in R.	lecture, laboratory	project	K_W02+ K_U02+ K_U03+ K_U05+ K_U07+ K_U18+ K_K01+ K_K02+ K_K05+	P6S_KK P6S_KO P6S_UW P6S_WG

Attention: Depending on the epidemic situation, verification of the achieved learning outcomes specified in the study program, in particular credits and examinations at the end of specific classes, can be implemented remotely (real-time meetings).

The syllabus of the module

Sem.	TK	The content	realized in	MEK
4	TK01	ntroduction: definition of time series, basic measures of time series , time series analysis by means of index methods, graphs and descriptive analysis in R.	W1-W4,	MEK01
4	TK02	Decomposition of time series: idea, smoothing by using a moving average, classical decomposition, elimination of trend and seasonality from data.	W5-W10, L7-L10	MEK02
4	TK03	Stationary and non-stationary models: review, identification, estimation of model parameters. Forecasting: simple forecasting methods, forecasting based on ARIMA models.	W11-W15, L11-L15	MEK03

The student's effort

The type of classes	The work before classes	The participation in classes	The work after classes
Lecture (sem. 4)		contact hours: 15.00 hours/sem.	complementing/reading through notes: 5.00 hours/sem. Studying the recommended bibliography: 5.00 hours/sem.
Laboratory (sem. 4)	The preparation for a Laboratory: 5.00 hours/sem.	contact hours: 15.00 hours/sem.	Finishing/Making the report: 10.00 hours/sem.
Advice (sem. 4)	The preparation for Advice: 2.00 hours/sem.	The participation in Advice: 2.00 hours/sem.
Credit (sem. 4)		The oral credit: 1.00 hours/sem.

The way of giving the component module grades and the final grade

The type of classes	The way of giving the final grade
Lecture	Presence on lectures.
Laboratory	Presence on exercises. Credit on the base of completed the laboratory exercises.
The final grade	The final grade is a grade obtained from project regarding the analysis of the selected time series.

Sample problems

Required during the exam/when receiving the credit
(-)

Realized during classes/laboratories/projects
(-)

Others
(-)

Can a student use any teaching aids during the exam/when receiving the credit : no

The contents of the module are associated with the research profile: no