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MATH 645 - Time Series |

Math 645 Time Series Analysis. This course will focus on the theory and application of methods for time series data. Emphasis will be in the following areas: This course discusses the modeling and forecasting of univariate and multivariate time series. Course topics include exploratory methods, white noise and random walk models, smoothing techniques, basic time series models for stationary and non-stationary data including autoregressive (AR), moving average (MA) and autoregressive moving average(ARMA) models as well as advanced models (ARCH, GARCH, etc.), regression methods for time series data, spectral analysis, and advanced topics in multivariate time series (vector autoregressive models, and state-space models). Applications to real-world data sets will be explored using the software package R. Examples are drawn from a variety of areas including business, economics, public policy, environment and ecology. Credits: 3 Prerequisites: MATH-503 Mathematical Statistics or equivalent; MATH-510 is optional but highly encouraged (Note: basic familiarity with R is expected). Textbook. No text required. Instructor will use own notes. Prerequisites: Math 137, Math 150 Restrictions: Must be enrolled in one of the following Levels: MN or MC Graduate Must be enrolled in one of the following Majors: Mathematics and Statistics 3.000 Credit hours 3.000 Lecture hours Levels: MN or MC Graduate Schedule Types: Lecture Mathematics Department Restrictions: Must be enrolled in one of the following Levels: MN or MC Graduate Must be enrolled in one of the following Fields of Study (Major, Minor, Concentration, or Certificate): Mathematics and Statistics Prerequisites: MATH 503 |

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