Time Series Analysis 2018

Time Series Analysis belongs to a broad range of Data Science. Its objective is: given a time series, or an ordered string of data points, predict its future values. Time series often arise when monitoring industrial processes, taking consecutive measurements of a quantity or tracking corporate business metrics. Time Series Analysis accounts for the fact that data points taken over time may have an internal structure, such as autocorrelation, trend, or seasonal variations that should be accounted for. Methods developed within Time Series Analysis are frequently used in other areas, like signal or image processing.

The course will cover the following subjects: Fast Fourier Transform - the power spectrum - smoothing and denoising - digital linear filters - "classic" linear models (AR, MA, ARMA, ARIMA, GARCH) - fractional models - Detrended Fluctuations Analysis - multivariate time series - wavelets - nonlinear prediction.

To complete the course, a student will need to attend the lectures and complete several (6-7, or something like that) simple home assignments.


Website »