CIS607 - Applied Business Forecasting

This course presents various statistical and machine learning techniques for forecasting business data. Students will learn how to describe the time series properties, such as the trend, cycles, seasonality, and noise. Moreover, they will learn how to build short-term and long-term forecasting models based on traditional statistical forecasting models including the classical time series models, Exponential Smoothing model, and ARIMA models, as well as machine learning algorithms such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). This course will use project-based learning to engage the students in real-life problem solving, and analysis will be done on Python, R, or EViews.
Prerequisite(s): CIS 621
Fulfills: 4+1
Credits: 3