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Exp-Smoothing.html
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<h1 class="title toc-ignore">Exponential Smoothing Models</h1>
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<p><em>For labs</em>: Click the ‘Rmd file’ link and download to your RWorkflow folder. Open RStudio. Switch to your RWorkflow project, by clicking button in top right (click create a new project if you did not do this step before). If you need the landings data, download here <a href="https://fish-forecast.github.io/Catch-Forecasting-INCOIS/landings.RData">here</a>.</p>
<ul>
<li><p>Lecture 1 <a href="Forecasting_4-1_-_Exp_Smoothing.html">Exponential Smoothing Models</a></p></li>
<li><p>Lab 1 Create forecasts with exponential smoothing models - <a href="Labs/R_scripts/Forecasting%204-1%20-%20Exp%20Smoothing.R">R Code from Lecture 1</a></p></li>
<li><p>Lab 2 Compare a set of models <a href="ETS%20Lab%201%20Evaluation.html">ETS Lab 1 Evaluation</a> - <a href="Labs/ETS%20Lab%201%20Evaluation.Rmd">Rmd file</a></p></li>
<li><p>Lecture 2 <a href="Forecasting_4-2_-_ETS_Seasonality.html">Seasonality with exponential smoothing and ARIMA models</a></p></li>
<li><p>Lab 3 Seasonal models - <a href="Labs/R_scripts/Forecasting%204-2%20-%20ETS%20Seasonality.R">R Code from Lecture 2</a> - <a href="https://fish-forecast.github.io/Catch-Forecasting-INCOIS/chinook.RData">chinook data</a></p></li>
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<p>Reference. Rob J Hyndman (lead on the forecast package) and George Athanasopoulos have an excellent online text on practical forecasting and exponential smoothing. Read <a href="https://otexts.org/fpp2/expsmooth.html">their chapter</a> on exponential smoothing to learn more about these models and how to use them.</p>
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