TrendTM | R Documentation |
It is the main function. It performs the factorization for a selected rank and a temporal structure with a selected tau if the selection is requested otherwise it is fixed
TrendTM(
Data_Series,
k_select = FALSE,
k_max = 20,
struct_temp = "none",
tau_select = FALSE,
tau_max = floor(n/2),
type_soft = "als"
)
Data_Series |
the data matrix with d rows and n columns containing the d temporal series with size n. |
k_select |
a boolean indicating if the rank of the matrix Data_Series will be selected. Default is FALSE. |
k_max |
the fixed rank of Data_Series if |
struct_temp |
a name indicating the temporal structure. Could be |
tau_select |
a boolean indicating if the parameter tau will be selected. This can be possible only when |
tau_max |
the fixed value for tau if |
type_soft |
the option |
The penalty constant(s) is(are) calibrated using the slope heuristic from package capushe. We adapt this heuristic as follows: the final dimension is the one correspind to the majority of the selected dimension for the considered different penalties.
A list containing
k_est
the selected rank if k_select==TRUE
or k_max
if k_select==FALSE
.
tau_est
the selected tau if tau_select==TRUE
or tau_max
if tau_select==FALSE
.
U_est
the component U of the decomposition of the final estimator M_est
.
V_est
the component V of the decomposition of the final estimator M_est
.
M_est
the estimation of M.
contrast
the Frobenius norm of Data_Series-M_est. This is a value when k_select==FALSE
and tau_select==FALSE
, a vector when k_select==TRUE
or tau_select==TRUE
, and a matrix when k_select==TRUE
and tau_select==TRUE
with k_max
rows and tau_max
columns.
data(Data_Series)
result <- TrendTM(Data_Series, k_max = 3)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.