autoregressive–moving-average (ARMA) models are a way to describe a (weakly) stationary stochastic process using autoregression (AR) and a moving average...

moving-average model. Autoregressive–moving-average model Autoregressive integrated moving average Autoregressive model Finite impulse response Infinite impulse...

econometrics, autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models are generalizations of the autoregressive moving average (ARMA)...

autoregressive fractionally integrated moving average models are time series models that generalize ARIMA (autoregressive integrated moving average)...

the moving-average (MA) model, it is a special case and key component of the more general autoregressive–moving-average (ARMA) and autoregressive integrated...

In statistics, a moving average (rolling average or running average or moving mean or rolling mean) is a calculation to analyze data points by creating...

weighted moving average (EWMA). Technically it can also be classified as an autoregressive integrated moving average (ARIMA) (0,1,1) model with no constant...

model is appropriate when the error variance in a time series follows an autoregressive (AR) model; if an autoregressive moving average (ARMA) model is...

can be either autoregressive (i.e. predicting how the segment continues, as GPTs do): for example given a segment "I like to eat", the model predicts "ice...

operator can be used, and this is a common notation for ARMA (autoregressive moving average) models. For example, ε t = X t − ∑ i = 1 p φ i X t − i = ( 1 −...

_{j}} . An example of a linear time series model is an autoregressive moving average model. Here the model for values { X t {\displaystyle X_{t}} } in...

different models: The behavior of the partial autocorrelation function mirrors that of the autocorrelation function for autoregressive and moving-average models...

applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past...

autoregressive moving average models and related ones such as autoregressive conditional heteroskedasticity (ARCH) and GARCH models for the modelling...

series models incorporate autocorrelation, such as unit root processes, trend-stationary processes, autoregressive processes, and moving average processes...

Otokar Arma, a military armoured vehicle Autoregressive–moving-average model, or ARMA model, a statistical model for time series 16S rRNA...

Random walk Autoregressive process Unit root Moving average process Autoregressive–moving-average model Autoregressive integrated moving average Vector autoregressive...

space include some autoregressive and moving average processes which are both subsets of the autoregressive moving average model. Models with a non-trivial...

series analysis Autoregressive model Moving average model Autoregressive moving average model Autoregressive integrated moving average model Anomaly time...

average Autoregressive integrated moving average Autoregressive model Autoregressive–moving-average model Auxiliary particle filter Average Average treatment...

about incoming data. Alternatively, the NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) algorithms which were developed as...

predictive modeling to form predictions called conditional expectations of the balances being audited using autoregressive integrated moving average (ARIMA)...

parametric methods include fitting to a moving-average model (MA) and to a full autoregressive moving-average model (ARMA). Frequency estimation is the process...

S. E.; Dickey, D. A. (1984). "Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order". Biometrika. 71 (3): 599–607. doi:10.1093/biomet/71...

Kashyap, R.L. (1982), "Optimal choice of AR and MA parts in autoregressive moving average models", IEEE Transactions on Pattern Analysis and Machine Intelligence...

test Ljung–Box test Autoregressive-moving-average model Breusch, T. S. (1978). "Testing for Autocorrelation in Dynamic Linear Models". Australian Economic...

bounds. A common way to model, and forecast, the spread for risk management purposes is by using autoregressive moving average models. Some other risks include:...

example, using an autoregressive or moving-average model). In these approaches, the task is to estimate the parameters of the model that describes the...

linear models (logistic regression, Poisson regression, Probit regression), etc. In case of forecasting, autoregressive moving average models and vector...

transfer dynamics. When modeling network data dynamics the traditional time series models, such as an autoregressive moving average model are not appropriate...