Autoregressive–movingaverage model
In the statistical analysis of time series, autoregressive–movingaverage (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression and the second for the moving average. 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 1971 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 model consists of two parts, an autoregressive (AR) part and a moving average (MA) part. The model is usually then referred to as the ARMA(p,q) model where p is the order of the autoregressive part and q is the order of the moving average part (as defined below).
ARIMA models can be estimated following the BoxJenkins approach.
Contents
Autoregressive model
The notation AR(p) refers to the autoregressive model of order p. The AR(p) model is written
where are parameters, is a constant, and the random variable is white noise.
Some constraints are necessary on the values of the parameters so that the model remains stationary. For example, processes in the AR(1) model with φ_{1} ≥ 1 are not stationary.
Movingaverage model
The notation MA(q) refers to the moving average model of order q:
where the θ_{1}, ..., θ_{q} are the parameters of the model, μ is the expectation of (often assumed to equal 0), and the , ,... are again, white noise error terms.
ARMA model
The notation ARMA(p, q) refers to the model with p autoregressive terms and q movingaverage terms. This model contains the AR(p) and MA(q) models,
The general ARMA model was described in the 1951 thesis of Peter Whittle, who used mathematical analysis (Laurent series and Fourier analysis) and statistical inference.^{[1]}^{[2]} ARMA models were popularized by a 1971 book by George E. P. Box and Jenkins, who expounded an iterative (Box–Jenkins) method for choosing and estimating them. This method was useful for loworder polynomials (of degree three or less).^{[3]}
Note about the error terms
The error terms are generally assumed to be independent identically distributed random variables (i.i.d.) sampled from a normal distribution with zero mean: ~ N(0,σ^{2}) where σ^{2} is the variance. These assumptions may be weakened but doing so will change the properties of the model. In particular, a change to the i.i.d. assumption would make a rather fundamental difference.
Specification in terms of lag operator
In some texts the models will be specified in terms of the lag operator L. In these terms then the AR(p) model is given by
where represents the polynomial
The MA(q) model is given by
where θ represents the polynomial
Finally, the combined ARMA(p, q) model is given by
or more concisely,
or
Alternative notation
Some authors, including Box, Jenkins & Reinsel use a different convention for the autoregression coefficients.^{[4]} This allows all the polynomials involving the lag operator to appear in a similar form throughout. Thus the ARMA model would be written as
Moreover, if we set , then we get an even more elegant formulation:
Fitting models
ARMA models in general can not be, after choosing p and q, fitted by least squares regression to find the values of the parameters which minimize the error term. It is generally considered good practice to find the smallest values of p and q which provide an acceptable fit to the data. For a pure AR model the YuleWalker equations may be used to provide a fit.
Finding appropriate values of p and q in the ARMA(p,q) model can be facilitated by plotting the partial autocorrelation functions for an estimate of p, and likewise using the autocorrelation functions for an estimate of q. Further information can be gleaned by considering the same functions for the residuals of a model fitted with an initial selection of p and q.
Brockwell & Davis recommend using AICc for finding p and q.^{[5]}
Implementations in statistics packages
 In R, the arima function (in standard package stats) is documented in ARIMA Modelling of Time Series. Extension packages contain related and extended functionality, e.g., the tseries package includes an arma function, documented in "Fit ARMA Models to Time Series"; the fracdiff package contains fracdiff() for fractionally integrated ARMA processes, etc. The CRAN task view on Time Series contains links to most of these.
 Mathematica has a complete library of time series functions including ARMA.^{[6]}
 MATLAB includes functions such as arma and ar to estimate AR, ARX (autoregressive exogenous), and ARMAX models. See System Identification Toolbox and Econometrics Toolbox for more information.
 Statsmodels Python module includes many models and functions for time series analysis, including ARMA. Formerly part of Scikitlearn it is now standalone and integrates well with Pandas. See here for more details.
 IMSL Numerical Libraries are libraries of numerical analysis functionality including ARMA and ARIMA procedures implemented in standard programming languages like C, Java, C# .NET, and Fortran.
 gretl can also estimate ARMA model, see here where it's mentioned.
 GNU Octave can estimate AR models using functions from the extra package octaveforge.
 Stata includes the function arima which can estimate ARMA and ARIMA models. See here for more details.
 SuanShu is a Java library of numerical methods, including comprehensive statistics packages, in which univariate/multivariate ARMA, ARIMA, ARMAX, etc. models are implemented in an objectoriented approach. These implementations are documented in "SuanShu, a Java numerical and statistical library".
 SAS has an econometric package, ETS, that estimates ARIMA models. See here for more details.
Applications
ARMA is appropriate when a system is a function of a series of unobserved shocks (the MA part)^{clarification needed} as well as its own behavior. For example, stock prices may be shocked by fundamental information as well as exhibiting technical trending and meanreversion effects due to market participants.
Generalizations
The dependence of X_{t} on past values and the error terms ε_{t} is assumed to be linear unless specified otherwise. If the dependence is nonlinear, the model is specifically called a nonlinear moving average (NMA), nonlinear autoregressive (NAR), or nonlinear autoregressive–movingaverage (NARMA) model.
Autoregressive–movingaverage models can be generalized in other ways. See also autoregressive conditional heteroskedasticity (ARCH) models and autoregressive integrated moving average (ARIMA) models. If multiple time series are to be fitted then a vector ARIMA (or VARIMA) model may be fitted. If the timeseries in question exhibits long memory then fractional ARIMA (FARIMA, sometimes called ARFIMA) modelling may be appropriate: see Autoregressive fractionally integrated moving average. If the data is thought to contain seasonal effects, it may be modeled by a SARIMA (seasonal ARIMA) or a periodic ARMA model.
Another generalization is the multiscale autoregressive (MAR) model. A MAR model is indexed by the nodes of a tree, whereas a standard (discrete time) autoregressive model is indexed by integers.
Note that the ARMA model is a univariate model. Extensions for the multivariate case are the Vector Autoregression (VAR) and Vector Autoregression MovingAverage (VARMA).
Autoregressive–movingaverage model with exogenous inputs model (ARMAX model)
The notation ARMAX(p, q, b) refers to the model with p autoregressive terms, q moving average terms and b exogenous inputs terms. This model contains the AR(p) and MA(q) models and a linear combination of the last b terms of a known and external time series . It is given by:
where are the parameters of the exogenous input .
Some nonlinear variants of models with exogenous variables have been defined: see for example Nonlinear autoregressive exogenous model.
Statistical packages implement the ARMAX model through the use of "exogenous" or "independent" variables. Care must be taken when interpreting the output of those packages, because the estimated parameters usually (for example, in R^{[7]} and gretl) refer to the regression:
where m_{t} incorporates all exogenous (or independent) variables:
See also
 Exponential smoothing
 Linear predictive coding
 Predictive analytics
 Arima, autoregressive integrated moving average
This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (August 2010) (Learn how and when to remove this template message) 
References
 ^ Hannan, Edward James (1970). Multiple time series. Wiley series in probability and mathematical statistics. New York: John Wiley and Sons.
 ^ Whittle, P. (1951). Hypothesis Testing in Time Series Analysis. Almquist and Wicksell. Whittle, P. (1963). Prediction and Regulation. English Universities Press. ISBN 0816611475.
 Republished as: Whittle, P. (1983). Prediction and Regulation by Linear LeastSquare Methods. University of Minnesota Press. ISBN 0816611483.
 ^ Hannan & Deistler (1988, p. 227): Hannan, E. J.; Deistler, Manfred (1988). Statistical theory of linear systems. Wiley series in probability and mathematical statistics. New York: John Wiley and Sons.
 ^ Box, George; Jenkins, Gwilym M.; Reinsel, Gregory C. (1994). Time Series Analysis: Forecasting and Control (Third ed.). PrenticeHall. ISBN 0130607746.
 ^ Brockwell, P. J.; Davis, R. A. (2009). Time Series: Theory and Methods (2nd ed.). New York: Springer. p. 273. ISBN 9781441903198.
 ^ Time series features in Mathematica
 ^ ARIMA Modelling of Time Series, R documentation
Further reading
 Mills, Terence C. (1990). Time Series Techniques for Economists. New York: Cambridge University Press. ISBN 0521343399.
 Percival, Donald B.; Walden, Andrew T. (1993). Spectral Analysis for Physical Applications. New York: Cambridge University Press. ISBN 052135532X.

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