Fitting autoregressive models for prediction

By Hirotugu Akaike. 1969

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Citations in the corpus

Recent Progress on Generative Adversarial Networks (GANs): A Survey (2019, background): “Now the generative models can be divided into three categories: Generative Adversarial Networks (GANs) [7], Variational Autoencoder (VAE) [9], and AutoRegressive Networks [10].”
Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models (2001, background): “The AIC criterion was introduced by Akaike (1969).”
Variable Selection in Predictive Regressions (2013, methodology): “A p-th order autoregression uses p lags and the model selection problem reduces to the determination of the lag length, p. Akaike (1969, 1970) propose to measure adequacy by the final prediction error E (yT+h − ŷT+h)2 which can be viewed as a weighted version of Mallows’ criterion with all weight…”
Order selection for same-realization predictions in autoregressive processes (2005, unknown): “4)], Akaike’s information criterion (AIC) [2], the final prediction error (FPE) method [1] and Sn(k) [27] are asymptotically efficient in the sense that no other selection criterion achieves a smaller limiting mean square prediction error criterion value.”
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics (2020, background): “, system identification) is a rapidly developing field [1], with a range of techniques including classic linear approaches [2, 3], dynamic mode decomposition (DMD) [4, 5] and Koopman theory [6–9], nonlinear autoregressive models [10, 11], neural networks [12–20], Gaussian process regression [21, 22]”
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery (2021, background): “An SEM is similar to an autoregressive model [2] in the sense that the value at the ith index depends on (possibly) all of the values before i.”
Hidden Markov models approach to the analysis of array CGH data (2004, unknown): “Note that DðLÞ 1⁄4 2 gives AIC or Akaike’s information criterion [1] and DðLÞ 1⁄4 logðLÞ one obtains the Schwartz BIC or Bayesian information criterion [17].”
Cointegration and Causality between Tourism and Poverty Reduction (2008, methodology): “The Akaike criteria (AIC; 1969, 1974) and the Schwartz (1978) Bayesian criteria (SBC) were used to determine the order of the vector autoregressive.”
The Properties of Automatic Gets Modelling (2005, unknown): “The first criterion, proposed by Akaike (1969, 1973) (denotedAIC for Akaike information criterion) penalizes the log-likelihood by 2n/T for n parameters and a sample size of T , but does not guarantee a consistent selection as the sample size diverges. Both the Schwarz (1978) information criterion, ”
Which Lag Length Selection Criteria Should We Employ (2006, unknown): “The most interesting finding of this study is that Akaike’s information criterion (AIC) and final prediction error (FPE) are superior than the other criteria under study in the case of small sample (60 observations and below), in the manners that they minimize the chance of under estimation while ma”