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. Both of these models are fitted to time series data either to better understand the data or to predict future points in the series (forecasting). ARIMA models 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.
Contents
- Definition
- Other special forms
- Differencing
- Forecasts using ARIMA models
- Forecast intervals
- Examples
- Information criteria
- Variations and extensions
- Software Implementations
- References
The AR part of ARIMA indicates that the evolving variable of interest is regressed on its own lagged (i.e., prior) values. The MA part indicates that the regression error is actually a linear combination of error terms whose values occurred contemporaneously and at various times in the past. The I (for "integrated") indicates that the data values have been replaced with the difference between their values and the previous values (and this differencing process may have been performed more than once). The purpose of each of these features is to make the model fit the data as well as possible.
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 (number of time lags) of the autoregressive model, d is the degree of differencing (the number of times the data have had past values subtracted), 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.
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).
ARIMA models can be estimated following the Box–Jenkins approach.
Definition
Given a time series of data Xt where t is an integer index and the Xt are real numbers, an ARMA(p,q) model is given by
or equivalently by
where
Assume now that the polynomial
An ARIMA(p,d,q) process expresses this polynomial factorisation property with p=p'−d, and is given by:
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, no ARIMA model with d > 0 is wide sense stationary.)
The above can be generalized as follows.
This defines an ARIMA(p,d,q) process with drift δ/(1 − Σφi).
Other special forms
The explicit identification of the factorisation of the autoregression polynomial into factors as above, can be extended to other cases, firstly to apply to the moving average polynomial and secondly to include other special factors. For example, having a factor
Identification and specification of appropriate factors in an ARIMA model can be an important step in modelling as it can allow a reduction in the overall number of parameters to be estimated, while allowing the imposition on the model of types of behaviour that logic and experience suggest should be there.
Differencing
Differencing in statistics refers to a transformation applied to time-series data in order to make it stationary. A stationary time series' properties do not depend on the time at which the series is observed.
In order to difference the data, the difference between consecutive observations is computed. Mathematically, this is shown as
Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series.
Sometimes it may be necessary to difference the data a second time to obtain a stationary time series, which is referred to as second order differencing:
Another method of differencing data is seasonal differencing, which involves computing the difference between an observation and the corresponding observation in the previous year. This is shown as:
The differenced data is then used for the estimation of an ARMA model.
Forecasts using ARIMA models
The ARIMA model can be viewed as a "cascade" of two models. The first is non-stationary:
while the second is wide-sense stationary:
Now forecasts can be made for the process
Forecast intervals
The forecast intervals (confidence intervals for forecasts) for ARIMA models are based on assumptions that the residuals are uncorrelated and normally distributed. If either of these assumptions does not hold, then the forecast intervals may be incorrect. For this reason, researchers plot the ACF and histogram of the residuals to check the assumptions before producing forecast intervals.
95% forecast interval:
For
For ARIMA(0,0,q),
In general, forecast intervals from ARIMA models will increase as the forecast horizon increases.
Examples
Some well-known special cases arise naturally or are mathematically equivalent to other popular forecasting models. For example:
Information criteria
To determine the order of a non-seasonal ARIMA model, a useful criterion is the Akaike information criterion (AIC) . It is written as
where L is the likelihood of the data, p is the order of the autoregressive part and q is the order of the moving average part. The parameter k in this criterion is defined as the number of parameters in the model being fitted to the data. For AIC, if k = 1 then c ≠ 0 and if k = 0 then c = 0.
The corrected AIC for ARIMA models can be written as
The Bayesian Information Criterion can be written as
The objective is to minimize the AIC, AICc or BIC values for a good model. The lower the value of one of these criteria for a range of models being investigated, the better the model will suit the data. It should be noted however that the AIC and the BIC are used for two completely different purposes. Whilst the AIC tries to approximate models towards the reality of the situation, the BIC attempts to find the perfect fit. The BIC approach is often criticized as there never is a perfect fit to real-life complex data; however, it is still a useful method for selection as it penalizes models more heavily for having more parameters than the AIC would.
AICc can only be used to compare ARIMA models with the same orders of differencing. For ARIMAs with different orders of differencing, RMSE can be used for model comparison.
Variations and extensions
A number of variations on the ARIMA model are commonly employed. If multiple time series are used then the
Software Implementations
Various packages that apply methodology like Box–Jenkins parameter optimization are available to find the right parameters for the ARIMA model.
ARIMA
fitting and forecasting.