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Lrnr_arima.html
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Lrnr_arima.html
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<title>Univariate ARIMA Models — Lrnr_arima • sl3</title>
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<h1>Univariate ARIMA Models</h1>
<small class="dont-index">Source: <a href='https://github.com/tlverse/sl3/blob/master/R/Lrnr_arima.R'><code>R/Lrnr_arima.R</code></a></small>
<div class="hidden name"><code>Lrnr_arima.Rd</code></div>
</div>
<div class="ref-description">
<p>This learner supports autoregressive integrated moving average model for
univariate time-series.</p>
</div>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p><code>R6Class</code> object.</p>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>Learner object with methods for training and prediction. See
<code><a href='Lrnr_base.html'>Lrnr_base</a></code> for documentation on learners.</p>
<h2 class="hasAnchor" id="parameters"><a class="anchor" href="#parameters"></a>Parameters</h2>
<ul>
<li><p><code>order</code>: An optional specification of the non-seasonal
part of the ARIMA model; the three integer components (p, d, q) are the
AR order, the degree of differencing, and the MA order. If order is
specified, then <code><a href='https://rdrr.io/r/stats/arima.html'>arima</a></code> will be called; otherwise,
<code><a href='https://pkg.robjhyndman.com/forecast/reference/auto.arima.html'>auto.arima</a></code> will be used to fit the "best" ARIMA
model according to AIC (default), AIC or BIC. The information criterion
to be used in <code><a href='https://pkg.robjhyndman.com/forecast/reference/auto.arima.html'>auto.arima</a></code> model selection can be
modified by specifying <code>ic</code> argument.</p></li>
<li><p><code>num_screen = 5</code>: The top n number of "most impotant" variables to
retain.</p></li>
<li><p><code>...</code>: Other parameters passed to <code><a href='https://rdrr.io/r/stats/arima.html'>arima</a></code> or
<code><a href='https://pkg.robjhyndman.com/forecast/reference/auto.arima.html'>auto.arima</a></code> function, depending on whether or not
<code>order</code> argument is provided.</p></li>
</ul>
<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_HarmonicReg.html'>Lrnr_HarmonicReg</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_ga.html'>Lrnr_ga</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>
<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='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='va'>task</span> <span class='op'><-</span> <span class='va'><a href='sl3_Task.html'>sl3_Task</a></span><span class='op'>$</span><span class='fu'>new</span><span class='op'>(</span>
data <span class='op'>=</span> <span class='va'>bsds</span>,
folds <span class='op'>=</span> <span class='va'>folds</span>,
covariates <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>
<span class='st'>"weekday"</span>, <span class='st'>"temp"</span>
<span class='op'>)</span>,
outcome <span class='op'>=</span> <span class='st'>"cnt"</span>
<span class='op'>)</span>
<span class='va'>arima_lrnr</span> <span class='op'><-</span> <span class='fu'><a href='Lrnr_base.html'>make_learner</a></span><span class='op'>(</span><span class='va'>Lrnr_arima</span><span class='op'>)</span>
<span class='va'>train_task</span> <span class='op'><-</span> <span class='fu'><a href='http://tlverse.org/origami/reference/fold_helpers.html'>training</a></span><span class='op'>(</span><span class='va'>task</span>, fold <span class='op'>=</span> <span class='va'>task</span><span class='op'>$</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'>$</span><span class='va'>folds</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span>
<span class='va'>arima_fit</span> <span class='op'><-</span> <span class='va'>arima_lrnr</span><span class='op'>$</span><span class='fu'>train</span><span class='op'>(</span><span class='va'>train_task</span><span class='op'>)</span>
<span class='va'>arima_preds</span> <span class='op'><-</span> <span class='va'>arima_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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