This learner supports various forms of nonlinear autoregression, including additive AR, neural nets, SETAR and LSTAR models, threshold VAR and VECM.
Lrnr_tsDyn
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
learner
Available built-in time series models. Currently available are listed by with availableModels() function.
m=1
embedding dimension.
size=1
number of hidden units in the neural network.
lag=1
Number of lags to include in each regime.
d=1
time delay.
include="const"
Type of deterministic regressors to include.
type="level"
Whether the variable is taken is level, difference or a mix as in the ADF test.
n.ahead="null"
The forecast horizon.
mL=m
autoregressive order for low regime.
mH=m
autoregressive order for high regime.
mM=NULL
autoregressive order for middle regime.
thDelay=0
Delay Time delay for the threshold variable.
common="none"
Indicates which elements are common to all regimes.
ML=seq_len(mL)
vector of lags for order for low.
MM=NULL
vector of lags for order for middle.
MH=seq_len(mH)
vector of lags for order for high.
nthresh=1
Threshold of the model.
trim=0.15
trimming parameter indicating the minimal percentage of observations in each regime.
sig=0.05
significance level for the tests to select the number of regimes.
control=list()
further arguments to be passed as control list
to optim
.
r=1
Number of cointegrating relationships.
model="VAR"
Model to estimate. Choices: VAR/VECM/TAR/MTAR.
I="level"
For VAR only: whether in the VAR variables are to be taken in levels or as a difference.
beta=NULL
For VECM only: imposed cointegrating value.
estim="2OLS"
Type of estimator for the VECM (two-step approach or Johansen MLE).
exogen=NULL
Inclusion of exogenous variables.
LRinclude="none"
Possibility to include in the long-run relationship and the ECT trend.
commonInter=FALSE
Whether the deterministic regressors are regime specific.
mTh=1
combination of variables with same lag order for the transition variable.
gamma=NULL
prespecified threshold values.
dummyToBothRegimes=TRUE
Whether the dummy in the one threshold model is applied to each regime.
max.iter=2
Number of iterations for the algorithm.
ngridBeta=50
Number of elements to search for the cointegrating value.
ngridTh=50
Number of elements to search for the threshold value.
th1
different possibilities to pre-specify an exact value, an interval or a central point for the search of the threshold.
th2
different possibilities to pre-specify an exact value or a central point for the search of the second threshold.
beta0=0
Additional regressors to include in the cointegrating relation.
...
Not currently used.
Other Learners: Custom_chain
,
Lrnr_HarmonicReg
, Lrnr_arima
,
Lrnr_bartMachine
, Lrnr_base
,
Lrnr_bilstm
, Lrnr_condensier
,
Lrnr_cv
,
Lrnr_define_interactions
,
Lrnr_expSmooth
,
Lrnr_glm_fast
, Lrnr_glmnet
,
Lrnr_glm
, Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_independent_binomial
,
Lrnr_lstm
, Lrnr_mean
,
Lrnr_nnls
, Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_randomForest
,
Lrnr_ranger
, Lrnr_rpart
,
Lrnr_rugarch
, Lrnr_sl
,
Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_subset_covariates
,
Lrnr_svm
, Lrnr_xgboost
,
Pipeline
, Stack
,
define_h2o_X
,
undocumented_learner