MTS.RdParameters description can be found at https://techtonique.github.io/nnetsauce/
MTS(
obj,
n_hidden_features = 5L,
activation_name = "relu",
a = 0.01,
nodes_sim = "sobol",
bias = TRUE,
dropout = 0,
direct_link = TRUE,
n_clusters = 2L,
cluster_encode = TRUE,
type_clust = "kmeans",
lags = 1L,
replications = NULL,
kernel = NULL,
agg = "mean",
seed = 123L,
backend = c("cpu", "gpu", "tpu"),
verbose = 0
)
# Example 1 -----
set.seed(123)
X <- matrix(rnorm(300), 100, 3)
obj <- sklearn$linear_model$ElasticNet()
obj2 <- MTS(obj)
obj2$fit(X)
#> MTS(dropout=0.0, kernel=None, obj=ElasticNet(), verbose=0.0)
obj2$predict()
#> series0 series1 series2
#> 1 0.09698047 -0.1014573 0.09947172
#> 2 0.09698047 -0.1014573 0.09947172
#> 3 0.09698047 -0.1014573 0.09947172
#> 4 0.09698047 -0.1014573 0.09947172
#> 5 0.09698047 -0.1014573 0.09947172
# Example 2 -----
set.seed(123)
X <- matrix(rnorm(300), 100, 3)
obj <- sklearn$linear_model$BayesianRidge()
obj2 <- MTS(obj)
obj2$fit(X)
#> MTS(dropout=0.0, kernel=None, obj=BayesianRidge(), verbose=0.0)
obj2$predict(return_std = TRUE)
#> DescribeResult(mean= series0 series1 series2
#> date
#> 2025-03-09 0.10 -0.05 0.10
#> 2025-03-10 0.10 -0.13 0.10
#> 2025-03-11 0.10 -0.13 0.10
#> 2025-03-12 0.10 -0.13 0.10
#> 2025-03-13 0.10 -0.13 0.10, lower= series0 series1 series2
#> date
#> 2025-03-09 -1.69 -1.94 -1.72
#> 2025-03-10 -1.69 -2.01 -1.72
#> 2025-03-11 -1.69 -2.01 -1.72
#> 2025-03-12 -1.69 -2.01 -1.72
#> 2025-03-13 -1.69 -2.01 -1.72, upper= series0 series1 series2
#> date
#> 2025-03-09 1.88 1.84 1.91
#> 2025-03-10 1.88 1.76 1.92
#> 2025-03-11 1.88 1.76 1.92
#> 2025-03-12 1.88 1.76 1.92
#> 2025-03-13 1.88 1.76 1.92)