Description
The IDWT function is the inverse of DWT(td_dwt_mle
). IDWT applies
inverse wavelet transforms on multiple sequences simultaneously. IDWT
takes as input the output tbl_teradata and meta tbl_teradata
generated by DWT(td_dwt_mle
) and outputs the sequences in time domain. Because
the IDWT output is comparable to the DWT(td_dwt_mle
) input, the inverse
transformation is also called the reconstruction.
Usage
td_idwt_mle (
coefficient = NULL,
meta.table = NULL,
input.columns = NULL,
sort.column = NULL,
partition.columns = NULL,
coefficient.sequence.column = NULL,
meta.table.sequence.column = NULL
)
Arguments
coefficient |
Required Argument. |
meta.table |
Required Argument. |
input.columns |
Required Argument. |
sort.column |
Required Argument. |
partition.columns |
Optional Argument. |
coefficient.sequence.column |
Optional Argument. |
meta.table.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_idwt_mle" which is a named list
containing objects of class "tbl_teradata".
Named list members can be referenced directly with the "$" operator
using following names:
output.table
output
Examples
# Get the current context/connection
con <- td_get_context()$connection
# Load example data.
# This example uses hourly climate data for five cities on a given day.
loadExampleData("dwt_example", "ville_climatedata")
# Create object(s) of class "tbl_teradata".
ville_climatedata <- tbl(con, "ville_climatedata")
# Example 1 -
# Apply DWT to sequences to create their coefficients and corresponding metadata.
td_dwt_out <- td_dwt_mle(data = ville_climatedata,
input.columns = c('temp_f','pressure_mbar','dewpoint_f'),
sort.column = "period",
wavelet = "db2",
partition.columns = c("city"),
level=2
)
# use the coefficient model tbl_teradata and the meta tbl_teradata
# generated by td_dwt_mle() function and apply td_idwt_mle() to the
# filtered coefficients to reconstruct the sequences.
td_idwt_out <- td_idwt_mle(coefficient = td_dwt_out$coefficient,
meta.table = td_dwt_out$meta.table,
input.columns = c("temp_f","pressure_mbar","dewpoint_f"),
sort.column = "waveletid",
partition.columns = c("city")
)