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Time series models for ecologists (TSME02)
1 October 2018 - 5 October 2018£275.00 - £500.00
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. The course is designed for attendees that have a basic knowledge of R and elementary statistics but not necessarily time series. Time Series data are ubiquitous in the physical sciences, and models for their behaviour enable scientists to understand temporal dynamics and predict future values. The course will cover a range of techniques from time series decomposition, seasonally adjusting, temporal autocorrelation and correlograms, 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 tutor.
Research postgraduates, practicing academics and primary investigators in spatial ecology and management and environmental professionals in government and industry.
Venue – PR statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map
Availability – 15 places
Duration – 5 days
Contact hours – Approx. 37 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments, minibus to and from meeting point and accommodation. Accommodation is multiple occupancy (max 4 people) single sex en-suite rooms. Arrival Sunday 30th September after 5am and departure Friday 5th October (accommodation must be vacated by 9am).
To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.
Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PR~statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PR~statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
Dr. Aristides (Aris) Moustakas
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
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Meet at Flat 2/1, 43 Cook Street, Glasgow G5 8JN at approx. 17:00 onwards
Monday 1st – Classes from 09:30 to 17:30
Introduction, example data sets
Revision: likelihood and inference
Revision: linear regression, GLMs, and exponential smoothing
Linear regression GLMs and exponential smoothing for time series
Analysing some example data sets
Tuesday 2nd – Classes from 09:30 to 17:30
Auto-regressive models and random walks
Moving averages and ARMA
Integrated models and ARIMA
Fitting ARIMA models with forecast
Wednesday 3rd Classes from 09:30 to 17:30
Additive and multiplicative time series
Model choice and forecasting
A walkthrough example time series analysis
Finding the best time series model for your data set
Thursday 4th – Classes from 09:30 to 17:30
Change points in the mean and variance of the data
State-space and change point models
Change point detection
Start analysing your own data set
Friday 4th – Classes from 09:30 to 16:00
Intervention analysis in time series
Creating bespoke time series models using Bayes
Bayesian causal impact of an intervention in time series
Bayesian causal impact intervention analysis
Open session: analyse your own data
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)