Date	Lecture topics	Lab topics
3 January	Course overview Properties of time series Data transformations Time series decomposition
5 January	Covariance & correlation Autocorrelation & Partial autocorrelation Cross correlation White noise	Matrices & matrix algebra Linear regression in matrix form Basic time series functions Autocorrelation
10 January	Random walks Autoregressive (AR) models Moving average (MA) models
12 January	Model estimation Maximum likelihood Bayesian estimation for this course	Simulating & fitting ARMA(p,q) models Bayesian estimation
17 January	Univariate state-space models Diagnostics for state-space models
19 January	Introduction to multivariate state-space models	Fitting state-space models
24 January	Including covariates (predictors) in models Seasonal effects Missing covariates Colinearity
26 January	Multi-model inference and selection model selection metrics besides AIC Cross-validation Forecast performance metrics	Model diagnostics Model selection
31 January	Univariate & multivariate dynamic linear models (DLMs)
2 February	Applications of dynamic linear models (DLMs)	Fitting DLMs
7 February	Forecasting with exponential smoothing models More forecast assessment
9 February	Overview of dynamic factor analysis (DFA)	Fitting DFA models
14 February	Overview of Bayesian estimation
16 February	Time series models with non-Gaussian errors Non-normal response variables	Fitting models with non-Gaussian errors Fitting zero-inflated models
21 February	Time series models with spatial autocorrelation
23 February	Intro to Gompertz models as AR(1) & ARX(1) Estimating species interactions	Fitting MARSS models for species interactions
28 February	Community dynamics & stability with MAR(1) models
2 March	Perturbation analysis	Fitting hierarchical models
7 March	Student presentations
9 March	Student presentations	Celebration!