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What (who) is autoregressive forecasting - definition

REPRESENTATION OF A TYPE OF RANDOM PROCESS

Autoregressive; AR(1); Autoregressive process; AR noise; Auto-regressive process; Auto-regression; AR process; Stochastic difference equation; AR model; Autoregression; Autoregressive forecasting; Autoregressive Modeling; Stochastic term; Yule-Walker equations; Burg algorithm; Burg method; Autoregressive models

Autoregressive model

In statistics, econometrics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence relation which should not be confused with differential equation).

https://en.wikipedia.org/wiki/Autoregressive_model

Forecasting (heating)

TERM

Forecasting (Heating)

Forecasting is a method of controlling building heating by calculating demand for heating energy that should be supplied to the building in each time unit. By combining physics of structures with meteorology, properties of the building, weather conditions including outdoor temperature, wind power and direction, as well as solar radiation can be taken into account.

https://en.wikipedia.org/wiki/Forecasting_(heating)

Autoregressive–moving-average model

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

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

Wikipedia

Autoregressive model

In statistics, econometrics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, behavior, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence relation which should not be confused with differential equation). Together with the moving-average (MA) model, it is a special case and key component of the more general autoregressive–moving-average (ARMA) and autoregressive integrated moving average (ARIMA) models of time series, which have a more complicated stochastic structure; it is also a special case of the vector autoregressive model (VAR), which consists of a system of more than one interlocking stochastic difference equation in more than one evolving random variable.

Contrary to the moving-average (MA) model, the autoregressive model is not always stationary as it may contain a unit root.

https://en.wikipedia.org/wiki/Autoregressive model