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Lrnr_HarmonicReg.html
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Lrnr_HarmonicReg.html
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<title>Harmonic Regression — Lrnr_HarmonicReg • sl3</title>
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<meta property="og:title" content="Harmonic Regression — Lrnr_HarmonicReg" />
<meta property="og:description" content="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
Hyndman et al. (2021)
) and
Hyndman and Khandakar (2008)
)." />
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<h1>Harmonic Regression</h1>
<small class="dont-index">Source: <a href='https://github.com/tlverse/sl3/blob/master/R/Lrnr_harmonicReg.R'><code>R/Lrnr_harmonicReg.R</code></a></small>
<div class="hidden name"><code>Lrnr_HarmonicReg.Rd</code></div>
</div>
<div class="ref-description">
<p>This learner fits first harmonics in a Fourier expansion to one
or more time series. Fourier decomposition relies on
<code><a href='https://pkg.robjhyndman.com/forecast/reference/fourier.html'>fourier</a></code>, and the time series is fit using
<code><a href='https://pkg.robjhyndman.com/forecast/reference/tslm.html'>tslm</a></code>. For further details on working with harmonic
regression for time-series with package <span class="pkg">forecast</span>, consider consulting
Hyndman et al. (2021)
) and
Hyndman and Khandakar (2008)
).</p>
</div>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>An <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6Class</a></code> object inheriting from
<code><a href='Lrnr_base.html'>Lrnr_base</a></code>.</p>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>A learner object inheriting from <code><a href='Lrnr_base.html'>Lrnr_base</a></code> with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of <code><a href='Lrnr_base.html'>Lrnr_base</a></code>.</p>
<h2 class="hasAnchor" id="parameters"><a class="anchor" href="#parameters"></a>Parameters</h2>
<ul>
<li><p><code>K</code>: Maximum order of the fourier terms. Passed to
<code><a href='https://pkg.robjhyndman.com/forecast/reference/fourier.html'>fourier</a></code>.</p></li>
<li><p><code>freq</code>: The frequency of the time series.</p></li>
<li><p><code>...</code>: Other parameters passed to <code><a href='https://pkg.robjhyndman.com/forecast/reference/fourier.html'>fourier</a></code>.</p></li>
</ul>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2021).
<em>forecast: Forecasting functions for time series and linear models</em>.
R package version 8.14, <a href='https://pkg.robjhyndman.com/forecast/'>https://pkg.robjhyndman.com/forecast/</a>.<br /><br /> Hyndman RJ, Khandakar Y (2008).
“Automatic time series forecasting: the forecast package for R.”
<em>Journal of Statistical Software</em>, <b>26</b>(3), 1--22.
<a href='https://www.jstatsoft.org/article/view/v027i03'>https://www.jstatsoft.org/article/view/v027i03</a>.</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p>Other Learners:
<code><a href='Custom_chain.html'>Custom_chain</a></code>,
<code><a href='Lrnr_arima.html'>Lrnr_arima</a></code>,
<code><a href='Lrnr_bartMachine.html'>Lrnr_bartMachine</a></code>,
<code><a href='Lrnr_base.html'>Lrnr_base</a></code>,
<code><a href='Lrnr_bayesglm.html'>Lrnr_bayesglm</a></code>,
<code><a href='Lrnr_bilstm.html'>Lrnr_bilstm</a></code>,
<code><a href='Lrnr_caret.html'>Lrnr_caret</a></code>,
<code><a href='Lrnr_cv_selector.html'>Lrnr_cv_selector</a></code>,
<code><a href='Lrnr_cv.html'>Lrnr_cv</a></code>,
<code><a href='Lrnr_dbarts.html'>Lrnr_dbarts</a></code>,
<code><a href='Lrnr_define_interactions.html'>Lrnr_define_interactions</a></code>,
<code><a href='Lrnr_density_discretize.html'>Lrnr_density_discretize</a></code>,
<code><a href='Lrnr_density_hse.html'>Lrnr_density_hse</a></code>,
<code><a href='Lrnr_density_semiparametric.html'>Lrnr_density_semiparametric</a></code>,
<code><a href='Lrnr_earth.html'>Lrnr_earth</a></code>,
<code><a href='Lrnr_expSmooth.html'>Lrnr_expSmooth</a></code>,
<code><a href='Lrnr_gam.html'>Lrnr_gam</a></code>,
<code><a href='Lrnr_gbm.html'>Lrnr_gbm</a></code>,
<code><a href='Lrnr_glm_fast.html'>Lrnr_glm_fast</a></code>,
<code><a href='Lrnr_glmnet.html'>Lrnr_glmnet</a></code>,
<code><a href='Lrnr_glm.html'>Lrnr_glm</a></code>,
<code><a href='Lrnr_grf.html'>Lrnr_grf</a></code>,
<code><a href='Lrnr_gru_keras.html'>Lrnr_gru_keras</a></code>,
<code><a href='Lrnr_gts.html'>Lrnr_gts</a></code>,
<code><a href='Lrnr_h2o_grid.html'>Lrnr_h2o_grid</a></code>,
<code><a href='Lrnr_hal9001.html'>Lrnr_hal9001</a></code>,
<code><a href='Lrnr_haldensify.html'>Lrnr_haldensify</a></code>,
<code><a href='Lrnr_hts.html'>Lrnr_hts</a></code>,
<code><a href='Lrnr_independent_binomial.html'>Lrnr_independent_binomial</a></code>,
<code><a href='Lrnr_lightgbm.html'>Lrnr_lightgbm</a></code>,
<code><a href='Lrnr_lstm_keras.html'>Lrnr_lstm_keras</a></code>,
<code><a href='Lrnr_mean.html'>Lrnr_mean</a></code>,
<code><a href='Lrnr_multiple_ts.html'>Lrnr_multiple_ts</a></code>,
<code><a href='Lrnr_multivariate.html'>Lrnr_multivariate</a></code>,
<code><a href='Lrnr_nnet.html'>Lrnr_nnet</a></code>,
<code><a href='Lrnr_nnls.html'>Lrnr_nnls</a></code>,
<code><a href='Lrnr_optim.html'>Lrnr_optim</a></code>,
<code><a href='Lrnr_pca.html'>Lrnr_pca</a></code>,
<code><a href='Lrnr_pkg_SuperLearner.html'>Lrnr_pkg_SuperLearner</a></code>,
<code><a href='Lrnr_polspline.html'>Lrnr_polspline</a></code>,
<code><a href='Lrnr_pooled_hazards.html'>Lrnr_pooled_hazards</a></code>,
<code><a href='Lrnr_randomForest.html'>Lrnr_randomForest</a></code>,
<code><a href='Lrnr_ranger.html'>Lrnr_ranger</a></code>,
<code><a href='Lrnr_revere_task.html'>Lrnr_revere_task</a></code>,
<code><a href='Lrnr_rpart.html'>Lrnr_rpart</a></code>,
<code><a href='Lrnr_rugarch.html'>Lrnr_rugarch</a></code>,
<code><a href='Lrnr_screener_augment.html'>Lrnr_screener_augment</a></code>,
<code><a href='Lrnr_screener_coefs.html'>Lrnr_screener_coefs</a></code>,
<code><a href='Lrnr_screener_correlation.html'>Lrnr_screener_correlation</a></code>,
<code><a href='Lrnr_screener_importance.html'>Lrnr_screener_importance</a></code>,
<code><a href='Lrnr_sl.html'>Lrnr_sl</a></code>,
<code><a href='Lrnr_solnp_density.html'>Lrnr_solnp_density</a></code>,
<code><a href='Lrnr_solnp.html'>Lrnr_solnp</a></code>,
<code><a href='Lrnr_stratified.html'>Lrnr_stratified</a></code>,
<code><a href='Lrnr_subset_covariates.html'>Lrnr_subset_covariates</a></code>,
<code><a href='Lrnr_svm.html'>Lrnr_svm</a></code>,
<code><a href='Lrnr_tsDyn.html'>Lrnr_tsDyn</a></code>,
<code><a href='Lrnr_ts_weights.html'>Lrnr_ts_weights</a></code>,
<code><a href='Lrnr_xgboost.html'>Lrnr_xgboost</a></code>,
<code><a href='Pipeline.html'>Pipeline</a></code>,
<code><a href='Stack.html'>Stack</a></code>,
<code><a href='define_h2o_X.html'>define_h2o_X</a>()</code>,
<code><a href='undocumented_learner.html'>undocumented_learner</a></code></p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://tlverse.org/origami'>origami</a></span><span class='op'>)</span>
</div><div class='output co'>#> <span class='message'>origami v1.0.3: Generalized Framework for Cross-Validation</span></div><div class='input'><span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-datatable.com'>data.table</a></span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>bsds</span><span class='op'>)</span>
<span class='co'># make folds appropriate for time-series cross-validation</span>
<span class='va'>folds</span> <span class='op'><-</span> <span class='fu'><a href='http://tlverse.org/origami/reference/make_folds.html'>make_folds</a></span><span class='op'>(</span><span class='va'>bsds</span>,
fold_fun <span class='op'>=</span> <span class='va'>folds_rolling_window</span>, window_size <span class='op'>=</span> <span class='fl'>500</span>,
validation_size <span class='op'>=</span> <span class='fl'>100</span>, gap <span class='op'>=</span> <span class='fl'>0</span>, batch <span class='op'>=</span> <span class='fl'>50</span>
<span class='op'>)</span>
<span class='co'># build task by passing in external folds structure</span>
<span class='va'>task</span> <span class='op'><-</span> <span class='va'><a href='sl3_Task.html'>sl3_Task</a></span><span class='op'>$</span><span class='fu'>new</span><span class='op'>(</span>
data <span class='op'>=</span> <span class='va'>bsds</span>,
folds <span class='op'>=</span> <span class='va'>folds</span>,
covariates <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>
<span class='st'>"weekday"</span>, <span class='st'>"temp"</span>
<span class='op'>)</span>,
outcome <span class='op'>=</span> <span class='st'>"cnt"</span>
<span class='op'>)</span>
<span class='co'># create tasks for taining and validation</span>
<span class='va'>train_task</span> <span class='op'><-</span> <span class='fu'><a href='http://tlverse.org/origami/reference/fold_helpers.html'>training</a></span><span class='op'>(</span><span class='va'>task</span>, fold <span class='op'>=</span> <span class='va'>task</span><span class='op'>$</span><span class='va'>folds</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span>
<span class='va'>valid_task</span> <span class='op'><-</span> <span class='fu'><a href='http://tlverse.org/origami/reference/fold_helpers.html'>validation</a></span><span class='op'>(</span><span class='va'>task</span>, fold <span class='op'>=</span> <span class='va'>task</span><span class='op'>$</span><span class='va'>folds</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span>
<span class='co'># instantiate learner, then fit and predict</span>
<span class='va'>HarReg_learner</span> <span class='op'><-</span> <span class='va'>Lrnr_HarmonicReg</span><span class='op'>$</span><span class='fu'>new</span><span class='op'>(</span>K <span class='op'>=</span> <span class='fl'>7</span>, freq <span class='op'>=</span> <span class='fl'>105</span><span class='op'>)</span>
</div><div class='output co'>#> <span class='message'>Registered S3 method overwritten by 'quantmod':</span>
#> <span class='message'> method from</span>
#> <span class='message'> as.zoo.data.frame zoo </span></div><div class='input'><span class='va'>HarReg_fit</span> <span class='op'><-</span> <span class='va'>HarReg_learner</span><span class='op'>$</span><span class='fu'>train</span><span class='op'>(</span><span class='va'>train_task</span><span class='op'>)</span>
<span class='va'>HarReg_preds</span> <span class='op'><-</span> <span class='va'>HarReg_fit</span><span class='op'>$</span><span class='fu'>predict</span><span class='op'>(</span><span class='va'>valid_task</span><span class='op'>)</span>
</div></pre>
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