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Autoregressive integrated moving average : ウィキペディア英語版
Autoregressive integrated moving average

In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series (forecasting). They are applied in some cases where data show evidence of non-stationarity, where an initial differencing step (corresponding to the "integrated" part of the model) can be applied to reduce the non-stationarity.〔For further information on Stationarity and Differencing see https://www.otexts.org/fpp/8/1〕
Non-seasonal ARIMA models are generally denoted ARIMA(p, d, q) where parameters ''p'', ''d'', and ''q'' are non-negative integers, p is the order of the Autoregressive model, d is the degree of differencing, and q is the order of the Moving-average model. Seasonal ARIMA models are usually denoted ARIMA(p, d, q)(P, D, Q)_m, where m refers to the number of periods in each season, and the uppercase P, D, Q refer to the autoregressive, differencing, and moving average terms for the seasonal part of the ARIMA model. ARIMA models form an important part of the Box-Jenkins approach to time-series modelling.
When two out of the three terms are zeros, the model may be referred to based on the non-zero parameter, dropping "AR", "I" or "MA" from the acronym describing the model. For example, ARIMA (1,0,0) is AR(1), ARIMA(0,1,0) is I(1), and ARIMA(0,0,1) is MA(1).
==Definition==

Given a time series of data X_t where t is an integer index and the X_t are real numbers, then an ARMA(''p' '',''q'') model is given by:

\left(
1 - \sum_^ \alpha_i L^i
\right) X_t
=
\left(
1 + \sum_^q \theta_i L^i
\right) \varepsilon_t \,

where L is the lag operator, the \alpha_i are the parameters of the autoregressive part of the model, the \theta_i are the parameters of the moving average part and the \varepsilon_t are error terms. The error terms \varepsilon_t are generally assumed to be independent, identically distributed variables sampled from a normal distribution with zero mean.
Assume now that the polynomial \left( 1 - \sum_^ \alpha_i L^i \right) has a unitary root of multiplicity ''d''. Then it can be rewritten as:
:
\left(
1 - \sum_^ \alpha_i L^i
\right)
=
\left(
1 - \sum_^ \phi_i L^i
\right)
\left(
1 - L
\right)^ .

An ARIMA(''p'',''d'',''q'') process expresses this polynomial factorisation property with ''p''=''p'−d'', and is given by:
:
\left(
1 - \sum_^p \phi_i L^i
\right)
\left(
1-L
\right)^d
X_t
=
\left(
1 + \sum_^q \theta_i L^i
\right) \varepsilon_t \,

and thus can be thought as a particular case of an ARMA(''p+d'',''q'') process having the autoregressive polynomial with ''d'' unit roots. (For this reason, every ARIMA model with ''d''>0 is not wide sense stationary.)
The above can be generalized as follows.
:
\left(
1 - \sum_^p \phi_i L^i
\right)
\left(
1-L
\right)^d
X_t
=
\delta + \left(
1 + \sum_^q \theta_i L^i
\right) \varepsilon_t \,

This defines an ARIMA(''p'',''d'',''q'') process with drift ''δ''/(1−Σ''φ''''i'').

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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