STAT 510: Applied Time Series Analysis

This course will give students a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is only the first step toward being able to confidently understand what trends may exist within a set of data and the complexities of using this information to make predictions or forecasts.

In this course, you will learn:

Understanding the features of time series data.
Understanding moving average models and partial autocorrelation as foundations for time series analysis.
Exploratory Data Analysis: Trends in Time Series Data.
Smoothing and eliminating patterns from time series data.
Understanding how periodograms work with time series data.
Implementing the ARMA and ARIMA time series models.
Identifying and analyzing distinct patterns of intervention impacts.
Analyzing the repeated measure design.
Using ARCH and AR models in multivariate time series settings.
Using spectral density estimate and analysis.
With time series data, we use fractional differencing and threshold models.

STAT 510: Applied Time Series Analysis