May 27, 2022  
2021-2022 Catalog 
    
2021-2022 Catalog

DS 318 - Forecasting with Time Series Data


Electricity consumption, infection rates, interest rates, rainfall, sales revenue, stock prices, temperature data. These are just a few examples of data collected overtime. Whether you wish to predict rainfall in your area over the next month or predict a trend in financial markets or in electricity consumption, time is an important factor that must be considered in your analysis if you want to develop a reasonable forecast of future values. This type of analysis is called time series analysis, with a time series being a series of data points ordered in time. In a time series, time is usually the independent variable, and the goal is usually to make a forecast for the future. In this course, you will perform time series analysis and use different forecasting techniques with time series data. You will consider the various aspects that come into play when dealing with time series, such as stationarity, seasonality, trends, autocorrelation, and spectral analysis. You will also use several models used in time series analysis, such as ARMA and ARIMA models. By studying examples of the application of timeseries techniques and then applying them yourself with sample data sets provided in the course, you will gain valuable skills for applying time series analysis techniques to real-world data and for building your own time series forecasts.

Prerequisites & Notes
Student has completed or is in process of completing any of the following course(s): DA 309 - Essentials of Biostatistics

Credits: 3