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}
}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…
1,973 Citations
A Note on Autoregressive Modeling
- MathematicsEconometric Theory
- 1994
This paper addresses the problem of estimating vector autoregressive models. An approach to handling nonstationary (integrated) time series is briefly discussed, but the main emphasis is upon the…
Autoregressive Model Fitting and Windows
- Mathematics
- 1994
Of the many pioneering contribution which Hirotugu Akaike has made to the field of Time Series Analysis his introduction of the concept of “order determination criteria” is one of the most…
Methods for Determining the Order of an Autoregressive-Moving Average Process: A
- Business
- 1985
In this survey the most important order determination methods are reviewed and their theoretical and practical relevance are discussed.
REGRESSION, AUTOREGRESSION MODELS
- Mathematics
- 1986
. The accuracy of least squares fitted regression autoregression models as approximations to more general stochastic structures is considered, attention being paid to the accuracy of the estimates of…
Order Choice in Nonlinear Autoregressive Models
- Computer Science, Mathematics
- 1995
The aim of this paper is to present a nonparametric approach that allows to estimate the autoregression order without limiting oneself to any restrictive parametric class of processes.
Properties of Predictors in Misspecified Autoregressive Time Series Models
- Mathematics
- 1985
Abstract This article investigates major effects of misspecification in stationary linear time series models when we fit a pth-order autoregressive model. The true model can be an autoregressive…
Lag space estimation in time series modelling
- Computer Science, Mathematics1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
- 1997
The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling, as it conditions the design of the model through the regressor vector.
Nonstationary and Seasonal Time Series Models
- Mathematics
- 2016
In this chapter we shall examine the problem of finding an appropriate model for a given set of observations {x 1 ,..., x n } that are not necessarily generated by a stationary time series. If the…
APPROXIMATION OF LINEAR SYSTEMS
- Mathematics
- 1987
The classical paradigm of statistics assumes that data is generated by a stochastic process whose structure is entirely known save for a fixed number of parameters, which partly explains the wide use of Fourier methods, which are non-parametric.
References
SHOWING 1-10 OF 12 REFERENCES
MULTIPLE TIME SERIES MODELLING.
- Mathematics
- 1968
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
- Mathematics
- 1968
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.
- Mathematics, Computer Science
- 1968
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.