naginterfaces.library.tsa.multi_inputmod_estim¶

naginterfaces.library.tsa.
multi_inputmod_estim
(mr, mt, para, xxy, kfc=1, kef=2, nit=1000, zsp=None, kpriv=0, io_manager=None)[source]¶ multi_inputmod_estim
fits a multiinput model relating one output series to the input series with a choice of three different estimation criteria: nonlinear least squares, exact likelihood and marginal likelihood. When no input series are present,multi_inputmod_estim
fits a univariate ARIMA model.For full information please refer to the NAG Library document for g13be
https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/g13/g13bef.html
 Parameters
 mrint, arraylike, shape
The orders vector of the ARIMA model for the output noise component.
, , and refer respectively to the number of autoregressive , moving average , seasonal autoregressive and seasonal moving average parameters.
, and refer respectively to the order of nonseasonal differencing, the order of seasonal differencing and the seasonal period.
 mtint, arraylike, shape
The transfer function model orders , and of each of the input series. The order parameters for input series are held in column . Row holds the value , row 2 holds the value and row 3 holds the value . For a simple input, .
Row 4 holds the value , where for a simple input, for a transfer function input for which no allowance is to be made for preobservation period effects, and for a transfer function input for which preobservation period effects will be treated by estimation of appropriate nuisance parameters.
When , any nonzero contents of rows 1, 2, and 3 of column are ignored.
 parafloat, arraylike, shape
Initial values of the multiinput model parameters. These are in order, firstly the ARIMA model parameters: values of parameters, values of parameters, values of parameters and values of parameters. These are followed by initial values of the transfer function model parameters , for the first of any input series and similarly for each subsequent input series. The final component of is the initial value of the constant , whether it is fixed or is to be estimated.
 xxyfloat, arraylike, shape
The columns of must contain the original, undifferenced values of each of the input series and the output series in that order.
 kfcint, optional
Must be set to if the constant is to remain fixed at its initial value, and if it is to be estimated.
 kefint, optional
Indicates the likelihood option.
Gives least squares.
Gives exact likelihood.
Gives marginal likelihood.
 nitint, optional
The maximum required number of iterations.
No change is made to any of the model parameters in array except that the constant (if ) and any relating to simple input series are estimated. (Apart from these, estimates are always derived for the nuisance parameters relating to any backforecasts and any preobservation period effects for transfer function inputs.)
 zspNone or float, arraylike, shape , optional
If , then must contain the four values used to control the strategy of the search procedure.
Contains , the value used to constrain the magnitude of the search procedure steps.
Contains , the multiplier which regulates the value of .
Contains , the value of the stationarity and invertibility test tolerance factor.
Contains , the value of the convergence criterion.
If before entry, default values of are supplied by the function.
These are , , and , respectively.
 kprivint, optional
Must not be set to , if it is required to monitor the course of the optimization. The course of the optimization is monitored by printing out at each iteration the iteration count (), the residual sum of squares (), the objective function () and a description and value for each of the parameters in the array. The descriptions are PHI for , THETA for , SPHI for , STHETA for , OMEGA/SI for in a simple input, OMEGA for in a transfer function input, DELTA for and CONSTANT for . In addition SERIES 1, SERIES 2, etc. indicate the input series relevant to the OMEGA and DELTA parameters.
must be set to if the printout of the above information is not required.
 io_managerFileObjManager, optional
Manager for I/O in this routine.
 Returns
 parafloat, ndarray, shape
The latest values of the estimates of these parameters.
 xxyfloat, ndarray, shape
If , remains unchanged on exit.
If , the columns of hold the corresponding values of the input component series in place of and the output noise component in place of , in that order.
 zspfloat, ndarray, shape
Contains the values, default or otherwise, used by the function.
 itcint
The number of iterations carried out.
Indicates that the only estimates obtained up to this point have been for the nuisance parameters relating to backforecasts, unless the marginal likelihood option is used, in which case estimates have also been obtained for simple input coefficients and for the constant (if ). This value of usually indicates a failure in a consequent step of estimating transfer function input preobservation period nuisance parameters.
Indicates that estimates have been obtained up to this point for the constant (if ), for simple input coefficients and for the nuisance parameters relating to the backforecasts and to transfer function input preobservation period effects.
 sdfloat, ndarray, shape
The values of the standard deviations corresponding to each of the parameters in . When the constant is fixed its standard deviation is returned as zero. When the values of are valid, the values of are usually also valid. However, if an exit value of = 3, 8 or 10, then the contents of will be indeterminate.
 cmfloat, ndarray, shape
The first rows and columns of contain the correlation coefficients relating to each pair of parameters in . All coefficients relating to the constant will be zero if the constant is fixed. The contents of will be indeterminate under the same conditions as .
 sfloat
The residual sum of squares, , at the latest set of valid parameter estimates.
 dfloat
The objective function, , at the latest set of valid parameter estimates.
 ndfint
The number of degrees of freedom associated with .
 resfloat, ndarray, shape
The values of the residuals relating to the differenced values of the output series. The remainder of the first terms in the array will be zero.
 sttffloat, ndarray, shape
The values of the state set array.
 nsttfint
The number of values in the state set array .
 Raises
 NagValueError
 (errno )
On entry, and there are no parameters in the model.
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, .
Constraint: , or .
 (errno )
On entry, .
Constraint: or .
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, and .
Constraint: , , and must be consistent.
 (errno )
On entry, and .
Constraint: , or .
 (errno )
The orders vector is invalid.
 (errno )
On entry, or during execution, one or more sets of delta parameters do not satisfy the stationarity or invertibility conditions.
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, .
Constraint: .
 (errno )
Unable to calculate the latest parameter estimates.
 (errno )
Failure in inversion of second derivative matrix.
 (errno )
On entry, or during execution, one or more sets of ARIMA parameters do not satisfy the stationarity or invertibility conditions.
 (errno )
On entry, too small.
Constraint: .
 (errno )
The routine has failed to converge after iterations.
 (errno )
On entry, .
is too small and the process failed to converge after iterations.
 Notes
In the NAG Library the traditional C interface for this routine uses a different algorithmic base. Please contact NAG if you have any questions about compatibility.
The Multiinput Model
The output series , for , is assumed to be the sum of (unobserved) components which are due respectively to the inputs , for .
Thus where is the error, or output noise component.
A typical component may be either
a simple regression component, (here is called a simple input), or
a transfer function model component which allows for the effect of lagged values of the variable, related to by
The noise is assumed to follow a (possibly seasonal) ARIMA model, i.e., may be represented in terms of an uncorrelated series, , by the hierarchy of equations
as outlined in Notes for uni_arima_estim.
Note: the orders appearing in each of the transfer function models and the ARIMA model are not necessarily the same; is the result of applying nonseasonal differencing of order and seasonal differencing of seasonality and order to the series : the differenced series is then of length ; the constant term parameter may optionally be held fixed at its initial value (usually, but not necessarily zero) rather than being estimated.
For the purpose of defining an estimation criterion it is assumed that the series is a sequence of independent Normal variates having mean and variance . An allowance has to be made for the effects of unobserved data prior to the observation period. For the noise component an allowance is always made using a form of backforecasting.
For each transfer function input, you have to decide what values are to be assumed for the preperiod terms and which are in theory necessary to recreate the component series , during the estimation procedure.
The first choice is to assume that all these values are zero. In this case, in order to avoid undesirable transient distortion of the early values , you are advised first to correct the input series by subtracting from all the terms a suitable constant to make the early values , close to zero. The series mean is one possibility, but for a series with strong trend the constant might be simply .
The second choice is to treat the unknown preperiod terms as nuisance parameters and estimate them along with the other parameters. This choice should be used with caution. For example, if and , it is equivalent to fitting to the data a decaying geometric curve of the form , for , along with the other inputs, this being the form of the transient. If the output contains a strong trend of this form, which is not otherwise represented in the model, it will have a tendency to influence the estimate of away from the value appropriate to the transfer function model.
In most applications the first choice should be adequate, with the option possibly being used as a refinement at the end of the modelling process. The number of nuisance parameters is then , with a corresponding loss of degrees of freedom in the residuals. If you align the input with the output by using in its place the shifted series , then setting in the transfer function model, there is some improvement in efficiency. On some occasions when the model contains two or more inputs, each with estimation of preperiod nuisance parameters, these parameters may be colinear and lead to failure of the function. The option must then be ‘switched off’ for one or more inputs.
The Estimation Criterion
This is a measure of how well a proposed set of parameters in the transfer function and noise ARIMA models matches the data. The estimation function searches for parameter values which minimize this criterion. For a proposed set of parameter values it is derived by calculating
the components as the responses to the input series using the equations (a) or (b) above,
the discrepancy between the output and the sum of these components, as the noise
the residual series from by reversing the recursive equations (i), (ii) and (iii) above.
This last step again requires treatment of the effect of unknown preperiod values of and other terms in the equations regenerating . This is identical to the treatment given in Notes for uni_arima_estim, and leads to a criterion which is a sum of squares function , of the residuals . It may be shown that the finite algorithm presented there is equivalent to taking the infinite set of past values , as (linear) nuisance parameters. There is no loss of degrees of freedom however, because the sum of squares function may be expressed as including the corresponding set of past residuals; see page 273 of Box and Jenkins (1976), who prove that
The function is the first of the three possible criteria, and is quite adequate for moderate to long series with no seasonal parameters. The second is the exact likelihood criterion which considers the past set not as simple nuisance parameters, but as unobserved random variables with known distribution. Calculation of the likelihood of the observed set requires theoretical integration over the range of the past set. Fortunately this yields a criterion of the form (whose minimization is equivalent to maximizing the exact likelihood of the data), where is exactly as before, and the multiplier is a function calculated from the ARIMA model parameters. The value of is always , and tends to for any fixed parameter set as the sample size tends to . There is a moderate computational overhead in using this option, but its use avoids appreciable bias in the ARIMA model parameters and yields a better conditioned estimation problem.
The third criterion of marginal likelihood treats the coefficients of the simple inputs in a manner analogous to that given to the past set . These coefficients, together with the constant term used to represent the mean of , are in effect treated as random variables with highly dispersed distributions. This leads to the criterion again, but with a different value of which now depends on the simple input series values . In the presence of a moderate to large number of simple inputs, the marginal likelihood criterion can counteract bias in the ARIMA model parameters which is caused by estimation of the simple inputs. This is particularly important in relatively short series.
multi_inputmod_estim
can be used with no input series present, to estimate a univariate ARIMA model for the output alone. The marginal likelihood criterion is then distinct from exact likelihood only if a constant term is being estimated in the model, because this is treated as an implicit simple input.The Estimation Procedure
This is the minimization of the estimation criterion or objective function (for deviance). The function uses an extension of the algorithm of Marquardt (1963). The step size in the minimization is inversely related to a parameter , which is increased or decreased by a factor at successive iterations, depending on the progress of the minimization. Convergence is deemed to have occurred if the fractional reduction of in successive iterations is less than a value , while .
Certain model parameters (in fact all excluding the s) are subject to stability constraints which are checked throughout to within a specified tolerance multiple of machine accuracy. Using the least squares criterion, the minimization may halt prematurely when some parameters ‘stick’ at a constraint boundary. This can happen particularly with short seasonal series (with a small number of whole seasons). It will not happen using the exact likelihood criterion, although convergence to a point on the boundary may sometimes be rather slow, because the criterion function may be very flat in such a region. There is also a smaller risk of a premature halt at a constraint boundary when marginal likelihood is used.
A positive, or zero number of iterations can be specified. In either case, the value of the objective function at iteration zero is presented at the initial parameter values, except for estimation of any preperiod terms for the input series, backforecasts for the noise series, and the coefficients of any simple inputs, and the constant term (unless this is held fixed).
At any later iteration, the value of is computed after reestimation of the backforecasts to their optimal values, corresponding to the model parameters presented at that iteration. This is not true for any preperiod terms for the input series which, although they are updated from the previous iteration, may not be precisely optimal for the parameter values presented, unless convergence of those parameters has occurred. However, in the case of marginal likelihood being specified, the coefficients of the simple inputs and the constant term are also reestimated together with the backforecasts at each iteration, to values which are optimal for the other parameter values presented.
Further Results
The residual variance is taken as , where (total number of parameters estimated), is the residual degrees of freedom. The preperiod nuisance parameters for the input series are included in the reduction of , as is the constant if it is estimated.
The covariance matrix of the vector of model parameter estimates is given by
where is the linearized least squares matrix taken from the final iteration of the algorithm of Marquardt. From this expression are derived the vector of standard deviations, and the correlation matrix of parameter estimates. These are approximations which are only valid asymptotically, and must be treated with great caution when the parameter estimates are close to their constraint boundaries.
The residual series is available upon completion of the iterations over the range corresponding to the differenced noise series .
Because of the algorithm used for backforecasting, these are only true residuals for , provided this is positive. Estimation of preperiod terms for the inputs will also tend to reduce the magnitude of the early residuals, sometimes severely.
The model component series and may optionally be returned in place of the supplied series values, in order to assess the effects of the various inputs on the output.
Forecasting Information
For the purpose of constructing forecasts of the output series at future time points using
multi_inputmod_forecast_state()
, it is not necessary to use the whole set of observations and , for . It is sufficient to retain a limited set of quantities constituting the ‘state set’ as follows: for each series which appears with lagged subscripts in equations (a), (b), (i), (ii) and (iii) above, include the values at times for up to the maximum lag associated with that series in the equations. Note that (i) implicitly includes past values of and intermediate differences of such as .If later observations of the series become available, it is possible to update the state set (without reestimating the model) using
multi_inputmod_update()
. If time series data is supplied with a previously estimated model, it is possible to construct the state set (and forecasts) usingmulti_inputmod_forecast()
.
 References
Box, G E P and Jenkins, G M, 1976, Time Series Analysis: Forecasting and Control, (Revised Edition), Holden–Day
Marquardt, D W, 1963, An algorithm for least squares estimation of nonlinear parameters, J. Soc. Indust. Appl. Math. (11), 431