Time-series data are commonly collected in both experimental and observational studies. Error enters these data through both the stochasticity in the underlying process, and uncertainty and error in the observations. State-space modeling provides an established and straightforward method for estimating time-series models with both process and measurement error. Additionally, state-space models provide a framework for dealing with multiple measurement time series and missing observations. Some examples of ecological applications where state-space models are routinely used include development of forecasting models in fisheries stock assessments, analysis of animal tracking data, and analysis of population or community time-series data. The morning lectures will introduce autoregressive models of the form X(t) = a(1)*X(t-1) + a(2)*X(t-2) … a(k)*X(t-k) + err(t) and show how familiar ecological models can be written in this form. Subsequent lectures will show how state-space models and Kalman filters can be used to estimate autoregressive models from data with measurement error and multiple measurement time series. The introductory lectures will be followed by three hands-on computer labs that illustrate common applications: estimation of forecasting models using multiple indicator time series, estimation of stochastic density-dependent population models from count data, and analysis of animal tracking data. The material will be presented at an introductory level, but students need to have a basic understanding of likelihood inference (e.g., familiarity with the terms likelihood function, likelihood surface, and probability density function). The computer labs will be done in R, but knowledge of R is not necessary. Students must provide laptops.