Time series analysis
1. Introduction to time series
a) What a time series is, how it differs from other data types, autocorrelation.
b) Basic time series models such as white noise, random walks and autoregressive models.
c) Connections to simple models of population dynamics, such as stochastic exponential growth.
2. AR, ARMA, structural changes, MAR, and multivariate (communities) statistics in time-series analysis.
3. State space models
4. Tools for time series analysis
5. Time series analysis of Swedish bird survey data.
The course structure includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for one full day (8:45 - 17:00; including 1 hour lunch and 2 fika breaks), equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students can use their own datasets as well.
Syllabus and other information
Syllabus
PNS0227 Time series analysis, 1.0 Credits
Subjects
Mathematical StatisticsEducation cycle
Postgraduate levelGrading scale
Language
EnglishPrior knowledge
Admitted to PhD-studies. Basic knowledge of statistics and RObjectives
This course is part of the course theme Advanced Statistics in Practice and aims to fill potential knowledge gaps by preparing students to analyze, interpret and report their data using the most up-to-date methods in R. Special attention will be given to discussion of questions springing from the students’ own work and the biological interpretation of data. The course is intended to deepen the students’ understanding of all aspects of ecological inferences, not as a cookbook of "how to"
Learning outcomes
1-Demonstrate the ability to identify relevant functions and packages in R for analyzing their own datasets.
2-Analyze data using R, including reporting advanced statistics, univariate and multivariate modelling and generation of graphs.
3-Interpret, think critically and draw conclusions on data analysis results.
Content
Time series analysis. Teachers: Jonas Knape (jonas.knape@slu.se) and Örjan Östman (orjan.ostman@slu.se).
- Introduction to time series
a) What a time series is, how it differs from other data types, autocorrelation.
b) Basic time series models such as white noise, random walks and autoregressive models.
c) Connections to simple models of population dynamics, such as stochastic exponential growth.
AR, ARMA, structural changes, MAR, and multivariate (communities) statistics in time-series analysis.
State space models
Tools for time series analysis
Time series analysis of Swedish bird survey data.
The course structure includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for one full day (8:45 - 17:00; including 1 hour lunch and 2 fika breaks), equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students can use their own datasets as well.
Additional information
The course is part of the course theme Advanced Statistics in Practice organized by Mohammad Bahram in collaboration with the NJ-faculty research schools ’Ecology-basics and applications’ and ’Focus on Soils and Water’ . The course plan for the theme and this course unit was accepted 20200922 by the steering group of the research school Ecology-basics and application.Course units of the theme Advanced Statistics in Practice 2021
• Time series analysis: Jonas Knape and Örjan Östman, Feb. 1
• GIS and spatial analysis: Alistair Auffret and Mohammad Bahram, Feb. 15 & Feb 18
• Meta-analysis: Julia Koricheva, April 12-13
• Getting more out of community data, Tomas Roslin and Giovanni Strona, May 3 and May 6
• Dealing with complexities of GLMs: Matt Low, May 31 & June 1
Responsible department
Department of Ecology