Time series models for ecologists (TSME01)
26 June 2017 - 30 June 2017£575.00 - £690.00
This course will cover time series analysis with a particular focus on applications in ecology. All methods will be illustrated using the free, open-source software package R. Time Series data are ubiquitous in the physical sciences, and models for their behaviour enable scientists to understand temporal dynamics and predict future values. Participants will be taught a wide range of suitable time series models for both discrete and continuous time systems. The course will cover a range of techniques from simple exponential smoothing and ARIMA modelling approaches up to complex Bayesian models. Participants will gain a deeper understanding of the models being fitted, and be able interpret the results appropriately. Participants are encouraged to bring their own data sets for discussion with the course tutors.
Research postgraduates, practicing academics and primary investigators in spatial ecology and management and environmental professionals in government and industry.
Venue – Northwest Atlantic Fisheries Centre, St. John’s – Google map – Directions
Availability – 32 places
Duration – 5 days
Contact hours – Approx. 37 hours
ECT’s – Equal to 3 ECT’s
Language – English
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• COURSE ONLY – Includes refreshments.
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Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links
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Monday 26th – Classes from 08:30 to 16:30
Class 1: Introduction, example data sets
Class 2: Revision: likelihood and inference
Class 3: Revision: linear regression, GLMs, and exponential smoothing
Tutor-guided practical: Linear regression GLMs and exponential smoothing for time series
Self-guided practical: Analysing some example data sets
Tuesday 27th – Classes from 08:30 to 16:30
Class 1: Auto-regressive models and random walks
Class 2: Moving averages and ARMA
Class 3: Integrated models and ARIMA
Tutor-guided practical: The forecast package in R
Self-guided practical: Fitting ARIMA models with forecast
Wednesday 28th – Classes from 08:30 to 16:30
Class 1: Including covariates: ARIMAX models
Class 2: Model choice and forecasting
Class 3: Creating bespoke time series models using Bayes
Tutor-guided practical: A walkthrough example time series analysis
Self-guided practical: Finding the best time series model for your data set
Thursday 29th – Classes from 08:30 to 16:30
Class 1: Modelling with seasonality and the frequency domain
Class 2: Stochastic volatility models and heteroskedasticity
Class 3: Fitting Bayesian time series models
Tutor-guided practical: Fitting time series models in Stan
Self-guided practical: Start analysing your own data set
Friday 30th – Classes from 08:30 to 15:30
Class 1: Models for continuous time series: Brownian Motion and Ornstien Uhlenbeck processes
Class 2: State-space and change point models
Class 3: Multivariate time series models and co-integration
Open session: analyse your own data