MTH4230 Time series and random processes in linear systems

When you finish this unit, you should be able to:

1. 1

   Analyse and evaluate stationary time series models, including autoregressive and moving average processes, and apply projection methods for forecasting;

2. 2

   Perform time and frequency domain analysis of time series data, applying techniques such as the Kalman filter and using ITSM to interpret and evaluate results;

3. 3

   Integrate theoretical understanding with practical implementation by applying stochastic models and computational tools to real data problems;

4. 4

   Extend and deepen understanding of time series methods through advanced model synthesis, rigorous analysis, and independent application to complex or novel datasets.

5. 5

   Apply the Kalman filter to random systems, demonstrating proficiency in both theoretical understanding and practical implementation.

6. 6

   Conduct analysis of time series data using the ITSM package, showcasing the ability to handle complex datasets and derive meaningful insights