Time Series Analysis

BDA

769

Time Series Analysis

In this course, we will learn and discuss about Time Series Analysis, a collection of statistical analysis methods for time series data in which a statistical data captures an object’s dynamic behavior. The classical definition of a statistical data is an i.e. random sample that is a collection of observations for many different objects that share the same probabilistic behavior (identically distributed) but do not have any relationship with each other (independent), and we can apply theories of statistical inference based on the classical probability theory. However, time series data is defined as which a statistical data collected its observations from a single object repeatedly over time. The classical probability theory that allows us to apply the conventional statistical analysis cannot be applied for this type of data because it is not i.i.d random sample by nature. Throughout this course, students will learn basic theories of Stochastic Process, a probability theory that applies for random variables defined on time, various time series analysis techniques based on stochastic process theories, and practice how to apply them for practical time series data in computer programming languages and software.

Fall, Spring

Graduate

No

036108