Time Series Prediction 2

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Department of Applied Informatics in Transportation

Synopsis:

Nonlinear forecasting methods, comparison with linear models. Artificial neural networks (feedforward multilayered neural networks, Kohonen's self organizing maps, recurrent networks), application of neural networks in forecasting. Learning algorithms, back-propagation, training, validation and testing data sets. Data preprocessing methods, data transformation, missing values and outliers processing. Input data coding, software realizations. Forecasting methodology and developing prediction models, application of forecasting models in different areas: transportation, environment, energy, banking.

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No time-table has been prepared for this course

The course is a part of the following study plans: