DATA643

Time series arise in multiple disciplines ranging from epidemiology and climate science to finance and predictive logistics whenever current values are influenced by previously observed ones. This course will cover the behavior and characteristics of time series along with decomposition and forecasting methodologies. We will begin by analyzing random walks, and move onto ARIMA, exponential smoothing, and spectral analysis. The course will conclude with contemporary deep learning methodologies such as LSTM and generative pretrained transformer based architectures.