ctf_tools | R Documentation |
Some useful tools for the cross-temporal forecast reconciliation of a linearly constrained (hierarchical/grouped) multiple time series.
ctf_tools(C, m, h = 1, Ut, nb, sparse = TRUE)
C |
(\mjseqnn_a \times n_b) cross-sectional (contemporaneous) matrix mapping the bottom level series into the higher level ones. |
m |
Highest available sampling frequency per seasonal cycle (max. order of temporal aggregation, \mjseqnm), or a subset of the \mjseqnp factors of \mjseqnm. |
h |
Forecast horizon for the lowest frequency (most temporally aggregated) time
series (default is |
Ut |
Zero constraints cross-sectional (contemporaneous) kernel matrix
\mjseqn(\mathbfU'\mathbfy = \mathbf0) spanning the null space valid
for the reconciled forecasts. It can be used instead of parameter
|
nb |
Number of bottom time series; if |
sparse |
Option to return sparse object (default is |
ctf list with:
Ht |
Full row-rank cross-temporal zero constraints (kernel) matrix coherent with \mjseqn\mathbfy = \mboxvec(\mathbfY'): \mjseqn\mathbfH'\mathbfy = \mathbf0. |
Hbrevet |
Complete, not full row-rank cross-temporal zero constraints (kernel) matrix coherent with \mjseqn\mathbfy = \mboxvec(\mathbfY'): \mjseqn\breve\mathbfH'\mathbfy = \mathbf0. |
Hcheckt |
Full row-rank cross-temporal zero constraints (kernel) matrix coherent with \mjseqn\check\mathbfy (structural representation): \mjseqn\check\mathbfH' \check\mathbfy = \mathbf0. |
Ccheck |
Cross-temporal aggregation matrix \mjseqn\check\mathbfC coherent with \mjseqn\check\mathbfy (structural representation). |
Scheck |
Cross-temporal summing matrix \mjseqn\check\mathbfS coherent with \mjseqn\check\mathbfy (structural representation). |
Fmat |
Cross-temporal summing matrix \mjseqn\widetilde\mathbfF coherent with \mjseqn\mathbfy = \mboxvec(\mathbfY'). |
hts list from hts_tools
.
thf list from thf_tools
.
Other utilities:
Cmatrix()
,
FoReco2ts()
,
agg_ts()
,
arrange_hres()
,
commat()
,
hts_tools()
,
lcmat()
,
oct_bounds()
,
residuals_matrix()
,
score_index()
,
shrink_estim()
,
thf_tools()
# One level hierarchy (na = 1, nb = 2) with quarterly data
obj <- ctf_tools(C = matrix(c(1, 1), 1), m = 4)
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