Lrnr_HarmonicReg: Harmonic Regression
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description Format Value Parameters References See Also Examples

This learner fits first harmonics in a Fourier expansion to one or more time series. Fourier decomposition relies on fourier, and the time series is fit using tslm. For further details on working with harmonic regression for time-series with package forecast, consider consulting \insertCiteforecast;textualsl3) and \insertCitehyndman2008forecast-jss;textualsl3).

An R6Class object inheriting from Lrnr_base.

A learner object inheriting from Lrnr_base with methods for training and prediction. For a full list of learner functionality, see the complete documentation of Lrnr_base.

K: Maximum order of the fourier terms. Passed to fourier.
freq: The frequency of the time series.
...: Other parameters passed to fourier.

\insertAllCited

Other Learners: Custom_chain, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, Lrnr_bilstm, Lrnr_caret, Lrnr_cv_selector, Lrnr_cv, Lrnr_dbarts, Lrnr_define_interactions, Lrnr_density_discretize, Lrnr_density_hse, Lrnr_density_semiparametric, Lrnr_earth, Lrnr_expSmooth, Lrnr_gam, Lrnr_ga, Lrnr_gbm, Lrnr_glm_fast, Lrnr_glmnet, Lrnr_glm, Lrnr_grf, Lrnr_gru_keras, Lrnr_gts, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_haldensify, Lrnr_hts, Lrnr_independent_binomial, Lrnr_lightgbm, Lrnr_lstm_keras, Lrnr_mean, Lrnr_multiple_ts, Lrnr_multivariate, Lrnr_nnet, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_polspline, Lrnr_pooled_hazards, Lrnr_randomForest, Lrnr_ranger, Lrnr_revere_task, Lrnr_rpart, Lrnr_rugarch, Lrnr_screener_augment, Lrnr_screener_coefs, Lrnr_screener_correlation, Lrnr_screener_importance, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_stratified, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_ts_weights, Lrnr_xgboost, Pipeline, Stack, define_h2o_X(), undocumented_learner

library(origami)
library(data.table)
data(bsds)

# make folds appropriate for time-series cross-validation
folds <- make_folds(bsds,
  fold_fun = folds_rolling_window, window_size = 500,
  validation_size = 100, gap = 0, batch = 50
)

# build task by passing in external folds structure
task <- sl3_Task$new(
  data = bsds,
  folds = folds,
  covariates = c(
    "weekday", "temp"
  ),
  outcome = "cnt"
)

# create tasks for taining and validation
train_task <- training(task, fold = task$folds[[1]])
valid_task <- validation(task, fold = task$folds[[1]])

# instantiate learner, then fit and predict
HarReg_learner <- Lrnr_HarmonicReg$new(K = 7, freq = 105)
HarReg_fit <- HarReg_learner$train(train_task)
HarReg_preds <- HarReg_fit$predict(valid_task)