Fitting autoregressive models for prediction

@article{Akaike1969FittingAM,
  title={Fitting autoregressive models for prediction},
  author={Hirotugu Akaike},
  journal={Annals of the Institute of Statistical Mathematics},
  year={1969},
  volume={21},
  pages={243-247}
}
  • H. Akaike
  • Published 1 December 1969
  • Computer Science
  • Annals of the Institute of Statistical Mathematics
This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models. The use of autoregressive representation of a stationary time series (or the innovations approach) in the analysis of time series has recently been attracting attentions of many research workers and it is expected that this time domain approach will give answers to many problems, such as the identification of noisy feedback systems, which… 
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References

SHOWING 1-10 OF 12 REFERENCES
MULTIPLE TIME SERIES MODELLING.
Abstract : The paper seeks to provide a general framework for the theory and practice of multivariate analysis of time series. It seeks to compare: (1) Spectral approaches to finding relations among
On the use of a linear model for the identification of feedback systems
SummaryA basic linear model of stationary stochastic processes is proposed for the analysis of linear feedback systems. The model suggests a simple computational procedure which gives estimates of
STATISTICAL SPECTRAL ANALYSIS (SINGLE CHANNEL CASE) IN 1968.
TLDR
This paper describes the view that to understand statistical spectral analysis in 1968 one must comprehend three distinct aspects: how to define the spectrum, how to compute the spectrum and how to interpret the spectrum.
Spectral Analysis Of Time Series
...
...