An Exponential Model for the Spectrum of a Scalar Time Series

Abstract

A new class of parametric models for the spectrum of a scalar time series is proposed, in which the logarithm of the spectral density function is represented by a finite Fourier series. Two alternative parameter estimation procedures are described, and the use of a fitted model to provide forecasts of future values is discussed. The model has been compared with the more conventional autoregressive/moving-average model, and the results of their comparison are given.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1972
Accession Number
AD0746160

Entities

People

  • P. Bloomfield

Organizations

  • Princeton University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Applied Mathematics
  • Bessel Functions
  • Coefficients
  • Complex Variables
  • Data Science
  • Efficiency
  • Fourier Series
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • Military Research
  • New York
  • Random Variables
  • Residuals
  • Sequences
  • Spectra
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Statistical inference.