Description

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,

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.

3.87 MB application/pdf

Download count: 0

Details

Contributors
Date Created
  • 2018
Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: Ph.D., Arizona State University, 2018
      Note type
      thesis
    • Includes bibliographical references (pages 119-131)
      Note type
      bibliography
    • Field of study: Statistics

    Citation and reuse

    Statement of Responsibility

    by Mario Giacomazzo

    Machine-readable links