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Что (кто) такое autoregressive sequence - определение

STATISTICAL MODEL USED IN TIME SERIES ANALYSIS

Autoregressive moving average; ARMAX; Autoregressive moving average model; ARMA model; Autoregressive moving-average model; Autoregressive-moving-average model

Sequence (music)

IMMEDIATE RESTATEMENT OF A MOTIF AT A HIGHER OR LOWER PITCH IN THE SAME VOICE

Modulating sequence; Real sequence; Tonal sequence; Modified sequence; False sequence; Descending fifths sequence; Rhythmic sequence

. Note that there are only four segments, continuingly higher, and that the segments continue by similar distance (seconds: C-D, D-E, etc.

https://en.wikipedia.org/wiki/Sequence_(music)

Autoregressive–moving-average model

In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). The general ARMA model was described in the 1951 thesis of Peter Whittle, Hypothesis testing in time series analysis, and it was popularized in the 1970 book by George E.

https://en.wikipedia.org/wiki/Autoregressive–moving-average_model

Recamán's sequence

ENDLESS SEQUENCE

User:Lugalde/Recamán's sequence; Draft:Recamán's sequence; Recaman's sequence; Recamán sequence

In mathematics and computer science, the Recamán's sequence (or Recaman's sequence) is a well known sequence defined by a recurrence relation. Because its elements are related to the previous elements in a straightforward way, they are often defined using recursion.

https://en.wikipedia.org/wiki/Recamán's_sequence

Википедия

Autoregressive–moving-average model

In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). The general ARMA model was described in the 1951 thesis of Peter Whittle, Hypothesis testing in time series analysis, and it was popularized in the 1970 book by George E. P. Box and Gwilym Jenkins.

Given a time series of data $X_{t}$ , the ARMA model is a tool for understanding and, perhaps, predicting future values in this series. The AR part involves regressing the variable on its own lagged (i.e., past) values. The MA part involves modeling the error term as a linear combination of error terms occurring contemporaneously and at various times in the past. The model is usually referred to as the ARMA(p,q) model where p is the order of the AR part and q is the order of the MA part (as defined below).

ARMA models can be estimated by using the Box–Jenkins method.

https://en.wikipedia.org/wiki/Autoregressive–moving-average model