TIME SERIS MODELS FOR ECOLOGISTS AND CLIMATOLOGISTS This course is being delivered by Dr Andrew Parnell and Dr Mike Salter- Townshend
It will run from 9th - 12th of May 2016 at SCENE (the Scottish Centre for Ec Ecology and the Natural Environment), Loch Lomond National Park, Glasgow. Course overview: This course will cover model-based time series analysis with a particular focus on applications in ecology and climatology. 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 takes a foundational Bayesian approach, which will enable participants to have a deeper understanding of the models being fitted, and to estimate all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors. Curriculum: Day 1: Basic concepts Class 1: Introduction; some example time series datasets; prediction vs explanat explanation Class 2: An introduction to Bayesian Statistics. Class 3: The AR(1) model Practical: revision on using R to load data, create plots and fit stati statistical models Round table discussion: understanding the output from a Bayesian model Day 2: ARIMA modelling Class 1: ARMA models for real data Class 2: ARIMA and sARIMA modelling Practical: An introduction to the Bayesian modelling language JAGS Round table discussion: understanding and running a JAGS model Day 3: Continuous time series Class 1: Brownian Motion and its application to real data sets Class 2: An introduction to Stochastic Volatility Modelling Practical: Fitting continuous time models in JAGS Round table discussion: Issues of continuous vs discrete time Day 4: Advanced time series models Class 1: Multivariate models Class 2: Fractional differencing and models using differential equations Practical: Running advanced models in JAGS Round table discussion: Bring your own data set The cost is £450 including lunches and course materials. An all-inclusive opti option is also available at £625; this includes breakfast, lunch, dinner, refr refreshments, accommodation and course materials. Participants will need a lapt laptop with a recent version of R This workshop is aimed at research postgraduates, practicing academics in ec ecology, climatology, evolution, meteorology, conservation and en environmental management, and environmental professionals in government and in industry. The workshop is delivered over 8 half-day sessions (see the detailed curriculum below). The session will consist of Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Finally, roun round-table discussions about the analysis requirements of attendees (opt (optional - bring your own data Assumed background: A basic understanding of statistical concepts. Such as re regression modelling and generalised linear models. Some understanding of Ba Bayesian Statistics is recommended but will be covered during the in introductory sessions. Familiarity with R. Ability to import/export data, ma manipulate data frames, fit basic statistical models & generate simple ex exploratory and diagnostic plots. Please send inquiries to [email protected] or visit the website w website www.prstatistics.co.uk Please fee free to distribute this information anywhere you think suitable.. http://prstatistics.co.uk/time%20series%20models%20for%20ecologists%20and%20clima climatologists/index.html Other upcoming courses - GENETIC DATA ANALYSIS USING SIAR; ; APPLIED BAY BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS; SPATIAL ANALYSIS OF ECO ECOLOGICAL DATA USING R; ADVANCING IN STATISTICAL MODELLING USING R; PYTHON FOR FOR BIOLOGISTS; INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS; ADVANCES IN IN DNA TAXONOMY; BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS; MUL MULTIVARIATE ANALYSIS OF SPATIAL DATA; MODEL BASE MULTIVARIATE ANALYSIS OF ABU ABUNDANCE DATA; Oliver Hooker PR~Statistics
