DL-315 Time Series Analysis & Forecast Tech †

Time series analysis involves statistical methods to provide meaning and understanding of temporal data. Forecasting uses models to predict future values based on historical observed values. In this course students will be introduced to both frequency and time domain methods. These methods will include spectral analysis, auto-correlation, and cross correlation. Specific models and techniques to be covered are regression, multivariate, moving average, autoregressive integrated moving average (ARIMA), seasonal, exponential smoothing, linear and non linear, and Markov chains. Students will use the R programming language for statistical analysis and model building.