The primary objective in time series analysis is forecasting. Raw data often exhibits nonstationary behavior: trends, seasonal cycles, and heteroskedasticity. After data is transformed to a weakly stationary process, autoregressive moving average (ARMA) models may capture the remaining temporal dynamics to improve forecasting. Estimation of ARMA can be performed through regressing current values on previous realizations and proxy innovations.
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- Partial requirement for: Ph.D., Arizona State University, 2018Note typethesis
- Includes bibliographical references (pages 119-131)Note typebibliography
- Field of study: Statistics