The function may be called by the names: g13dlc or nag_tsa_multi_diff.
3Description
For certain time series it may first be necessary to difference the original data to obtain a stationary series before calculating autocorrelations, etc. This function also allows you to apply either a square root or a log transformation to the original time series to stabilize the variance if required.
If the order of differencing required for the th series is , then the differencing operator is defined by , where is the backward shift operator; that is, . Let denote the maximum of the orders of differencing, , over the series. The function computes values of the differenced/transformed series , for , as follows:
where are the transformed values of the original -dimensional time series .
The differencing parameters , for and , must be supplied by you. If the th series does not require differencing, then .
4References
Box G E P and Jenkins G M (1976) Time Series Analysis: Forecasting and Control (Revised Edition) Holden–Day
Wei W W S (1990) Time Series Analysis: Univariate and Multivariate Methods Addison–Wesley
5Arguments
1: – IntegerInput
On entry: , the dimension of the multivariate time series.
Constraint:
.
2: – IntegerInput
On entry: , the number of observations in the series, prior to differencing.
Constraint:
.
3: – const doubleInput
On entry: must contain the th series at time , for and .
4: – const IntegerInput
On entry: indicates whether the th series is to be transformed, for .
A square root transformation is used.
No transformation is used.
A log transformation is used.
Constraint:
, or , for .
5: – const IntegerInput
On entry: the order of differencing for each series, .
Constraint:
, for .
6: – const doubleInput
Note: the dimension, dim, of the array delta
must be at least
, where .
On entry: if
then must be set to , for and .
7: – doubleOutput
Note: the dimension, dim, of the array w
must be at least
, where .
On exit: contains the value of , for and .
8: – Integer *Output
On exit: the number of differenced values, , in the series, where .
9: – NagError *Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).
6Error Indicators and Warnings
NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument had an illegal value.
NE_INT
On entry, .
Constraint: .
On entry, .
Constraint: .
NE_INT_ARRAY
On entry, and .
Constraint: .
On entry, , and .
Constraint: .
On entry, .
Constraint: , or .
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_TRANSFORMATION
On entry, one (or more) of the transformations requested is invalid. Check that you are not trying to log or square-root a series, some of whose values are negative.
7Accuracy
The computations are believed to be stable.
8Parallelism and Performance
Background information to multithreading can be found in the Multithreading documentation.
g13dlc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.
9Further Comments
The same differencing operator does not have to be applied to all the series. For example, suppose we have , and wish to apply the second-order differencing operator to the first series and the first-order differencing operator to the second series:
Then , , and
10Example
A program to difference (non-seasonally) each of two time series of length . No transformation is to be applied to either of the series.