To provide a strong foundation in predictive analytics with special focus on time series data. It covers a thorough discussion of the widely used time series techniques and their demonstration using industry based data sets
Upon completion of this course, students will be able to know
Extract useful information from large and complex data sets
Recognize patterns and trends in the data bases and model them.
Use appropriate modeling tools from the set of classical methods, ARIMA, filters and other smoothening techniques, neural nets and state space models etc in order to arrive accurate forecasts.
Use the most powerful and sophisticated routines in R for time series data analysis.
- Introduction to Time Series Data
- Classical Time Series Analysis and other Approaches- Decomposition of Time Series, Smoothening Techniques, Filters, Neural Net
- Stationary and Non Stationary Models-Types of Trends, Unit Root Tests
- Box-Jenkis Approcah
- Non-Linear Models : Basics
- Spectral Analysis-Spectrum- Discrete Fourier Transformation
- Multivariate Models-VAR-Cointegration-ECM
- State Space Models