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<h1 id="config-reference">Config Reference</h1>
<p><code>DecompositionConfig</code> is the single runtime contract shared by Python, CLI,
docs examples, and machine-facing schema exports.</p>
<p>Current package version target: <code>0.1.1</code>.</p>
<h2 id="top-level-fields">Top-level fields</h2>
<table>
<thead>
<tr>
<th>Field</th>
<th>Type</th>
<th>Default</th>
<th>Semantics</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>method</code></td>
<td><code>str</code></td>
<td>required</td>
<td>Registered method name such as <code>SSA</code>, <code>STD</code>, <code>STDR</code>, or <code>MSSA</code>.</td>
</tr>
<tr>
<td><code>params</code></td>
<td><code>dict[str, Any]</code></td>
<td><code>{}</code></td>
<td>Method-specific parameters documented below.</td>
</tr>
<tr>
<td><code>return_components</code></td>
<td><code>list[str] \| None</code></td>
<td><code>None</code></td>
<td>Compatibility field; retained methods return the normalized result object.</td>
</tr>
<tr>
<td><code>backend</code></td>
<td><code>auto \| native \| python \| gpu</code></td>
<td><code>auto</code></td>
<td>Backend preference. <code>native</code> requires an available native kernel.</td>
</tr>
<tr>
<td><code>speed_mode</code></td>
<td><code>exact \| fast</code></td>
<td><code>exact</code></td>
<td>Accuracy policy. Native <code>SSA</code> uses exact SVD in <code>exact</code> and an iterative approximation in <code>fast</code>.</td>
</tr>
<tr>
<td><code>profile</code></td>
<td><code>bool</code></td>
<td><code>False</code></td>
<td>Attach runtime metadata or produce profile reports when routed through the profiler.</td>
</tr>
<tr>
<td><code>device</code></td>
<td><code>str \| None</code></td>
<td><code>cpu</code></td>
<td>Reserved device selector; retained methods are CPU workflows unless a wrapper says otherwise.</td>
</tr>
<tr>
<td><code>n_jobs</code></td>
<td><code>int</code></td>
<td><code>1</code></td>
<td>Parallelism hint for wrappers that support it.</td>
</tr>
<tr>
<td><code>seed</code></td>
<td><code>int \| None</code></td>
<td><code>42</code></td>
<td>Seed used by approximate or randomized paths where relevant.</td>
</tr>
<tr>
<td><code>channel_names</code></td>
<td><code>list[str] \| None</code></td>
<td><code>None</code></td>
<td>Optional labels for aligned multivariate channels.</td>
</tr>
</tbody>
</table>
<h2 id="complete-examples">Complete examples</h2>
<h3 id="univariate-ssa">Univariate SSA</h3>
<pre><code class="language-json">{
&quot;backend&quot;: &quot;auto&quot;,
&quot;method&quot;: &quot;SSA&quot;,
&quot;params&quot;: {
&quot;primary_period&quot;: 12,
&quot;rank&quot;: 6,
&quot;window&quot;: 24
},
&quot;seed&quot;: 42,
&quot;speed_mode&quot;: &quot;exact&quot;
}
</code></pre>
<h3 id="seasonal-std">Seasonal STD</h3>
<pre><code class="language-json">{
&quot;backend&quot;: &quot;auto&quot;,
&quot;method&quot;: &quot;STD&quot;,
&quot;params&quot;: {
&quot;period&quot;: 12
},
&quot;speed_mode&quot;: &quot;exact&quot;
}
</code></pre>
<h3 id="multivariate-mssa">Multivariate MSSA</h3>
<pre><code class="language-json">{
&quot;backend&quot;: &quot;auto&quot;,
&quot;channel_names&quot;: [
&quot;channel_a&quot;,
&quot;channel_b&quot;,
&quot;channel_c&quot;
],
&quot;method&quot;: &quot;MSSA&quot;,
&quot;params&quot;: {
&quot;primary_period&quot;: 12,
&quot;rank&quot;: 6,
&quot;window&quot;: 24
},
&quot;speed_mode&quot;: &quot;exact&quot;
}
</code></pre>
<h2 id="method-specific-parameters">Method-specific parameters</h2>
<h3 id="amd_block"><code>AMD_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>multiscale_windows</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Smoothing windows averaged into the multiscale trend.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;AMD_BLOCK&quot;,
&quot;params&quot;: {
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;multiscale_windows&quot;: [
13,
25,
49
],
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="autoformer_block"><code>AUTOFORMER_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.moving_mean</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>moving_avg</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average window used by the extracted forecasting block.</td>
</tr>
<tr>
<td><code>window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Alias for moving_avg.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Period hint used to derive the moving-average window.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;AUTOFORMER_BLOCK&quot;,
&quot;params&quot;: {
&quot;moving_avg&quot;: 25,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="ceemdan"><code>CEEMDAN</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.imfs</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>trials</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>50</code></td>
<td>Number of noise-assisted ensemble trials.</td>
</tr>
<tr>
<td><code>noise_width</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.05</code></td>
<td>Relative width of the injected noise.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Period hint for grouping IMFs into season and trend.</td>
</tr>
<tr>
<td><code>fs</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1.0</code></td>
<td>Sampling frequency used by grouping heuristics.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;CEEMDAN&quot;,
&quot;params&quot;: {
&quot;noise_width&quot;: 0.05,
&quot;primary_period&quot;: 12,
&quot;trials&quot;: 20
}
}
</code></pre>
<h3 id="delelstm_block"><code>DELELSTM_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>alpha</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.4</code></td>
<td>Holt level smoothing coefficient.</td>
</tr>
<tr>
<td><code>beta</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.2</code></td>
<td>Holt slope smoothing coefficient.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;DELELSTM_BLOCK&quot;,
&quot;params&quot;: {
&quot;alpha&quot;: 0.2,
&quot;beta&quot;: 0.1,
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="dlinear_block"><code>DLINEAR_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.moving_mean</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>moving_avg</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average window used by the extracted forecasting block.</td>
</tr>
<tr>
<td><code>window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Alias for moving_avg.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Period hint used to derive the moving-average window.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;DLINEAR_BLOCK&quot;,
&quot;params&quot;: {
&quot;moving_avg&quot;: 25,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="emd"><code>EMD</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.imfs</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>n_imfs</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Maximum number of intrinsic mode functions to retain.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Period hint for grouping IMFs into season and trend.</td>
</tr>
<tr>
<td><code>trend_imfs</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit IMF indexes assigned to trend.</td>
</tr>
<tr>
<td><code>season_imfs</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit IMF indexes assigned to season.</td>
</tr>
<tr>
<td><code>fs</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1.0</code></td>
<td>Sampling frequency used by grouping heuristics.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;EMD&quot;,
&quot;params&quot;: {
&quot;n_imfs&quot;: 4,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="freqmoe_block"><code>FREQMOE_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>trend_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average trend window.</td>
</tr>
<tr>
<td><code>num_bands</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>4</code></td>
<td>Number of frequency bands in the mixture.</td>
</tr>
<tr>
<td><code>expert_width</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>64</code></td>
<td>Frequency expert width used by the scaffold.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;FREQMOE_BLOCK&quot;,
&quot;params&quot;: {
&quot;expert_width&quot;: 64,
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;num_bands&quot;: 4,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="gabor_cluster"><code>GABOR_CLUSTER</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.clusters</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>model</code></td>
<td>GaborClusterModel | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>In-memory trained clustering model.</td>
</tr>
<tr>
<td><code>model_path</code></td>
<td>str | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Path to a serialized trained clustering model.</td>
</tr>
<tr>
<td><code>max_clusters</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Optional cap on clusters used during reconstruction.</td>
</tr>
<tr>
<td><code>trend_freq_thr</code></td>
<td>float | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Frequency threshold used for trend-like atoms.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;GABOR_CLUSTER&quot;,
&quot;params&quot;: {
&quot;model_path&quot;: &quot;path/to/trained-gabor-model.pkl&quot;
}
}
</code></pre>
<h3 id="inparformer_block"><code>INPARFORMER_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>trend_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average trend window.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;INPARFORMER_BLOCK&quot;,
&quot;params&quot;: {
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;primary_period&quot;: 12,
&quot;trend_window&quot;: 25
}
}
</code></pre>
<h3 id="leddam_block"><code>LEDDAM_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.ld_trend</code>, <code>components.kernel</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>kernel_size</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>25</code></td>
<td>Odd Gaussian smoothing kernel size.</td>
</tr>
<tr>
<td><code>sigma</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1.0</code></td>
<td>Gaussian smoothing kernel sigma.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;LEDDAM_BLOCK&quot;,
&quot;params&quot;: {
&quot;kernel_size&quot;: 25,
&quot;sigma&quot;: 1.0
}
}
</code></pre>
<h3 id="ma_baseline"><code>MA_BASELINE</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>trend_window</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>7</code></td>
<td>Moving-average window used for the trend estimate.</td>
</tr>
<tr>
<td><code>season_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Optional period for a simple seasonal average.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;MA_BASELINE&quot;,
&quot;params&quot;: {
&quot;season_period&quot;: 12,
&quot;trend_window&quot;: 7
}
}
</code></pre>
<h3 id="memd"><code>MEMD</code></h3>
<ul>
<li>Input mode: <code>multivariate</code></li>
<li>Maturity: <code>optional-backend</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.imfs</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Shared period hint for grouping multivariate IMFs.</td>
</tr>
<tr>
<td><code>trend_modes</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit mode indexes assigned to trend.</td>
</tr>
<tr>
<td><code>season_modes</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit mode indexes assigned to season.</td>
</tr>
<tr>
<td><code>fs</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1.0</code></td>
<td>Sampling frequency used by grouping heuristics.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;MEMD&quot;,
&quot;params&quot;: {
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="moving_average_decomposition_block"><code>MOVING_AVERAGE_DECOMPOSITION_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.moving_mean</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>moving_avg</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average window used by the extracted forecasting block.</td>
</tr>
<tr>
<td><code>window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Alias for moving_avg.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Period hint used to derive the moving-average window.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;MOVING_AVERAGE_DECOMPOSITION_BLOCK&quot;,
&quot;params&quot;: {
&quot;moving_avg&quot;: 25,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="mssa"><code>MSSA</code></h3>
<ul>
<li>Input mode: <code>multivariate</code></li>
<li>Maturity: <code>flagship</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.elementary</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>window</code></td>
<td>int</td>
<td style="text-align: right;">yes</td>
<td>required</td>
<td>Shared embedding window length for aligned channels.</td>
</tr>
<tr>
<td><code>rank</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Number of shared elementary components to retain.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant shared period used by automatic grouping.</td>
</tr>
<tr>
<td><code>fs</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1.0</code></td>
<td>Sampling frequency used by frequency-based grouping.</td>
</tr>
<tr>
<td><code>trend_components</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit component indexes assigned to trend.</td>
</tr>
<tr>
<td><code>season_components</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit component indexes assigned to season.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;backend&quot;: &quot;auto&quot;,
&quot;channel_names&quot;: [
&quot;channel_a&quot;,
&quot;channel_b&quot;,
&quot;channel_c&quot;
],
&quot;method&quot;: &quot;MSSA&quot;,
&quot;params&quot;: {
&quot;primary_period&quot;: 12,
&quot;rank&quot;: 6,
&quot;window&quot;: 24
},
&quot;speed_mode&quot;: &quot;exact&quot;
}
</code></pre>
<h3 id="mstl"><code>MSTL</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.seasonal_terms</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>periods</code></td>
<td>list[int]</td>
<td style="text-align: right;">yes</td>
<td>required</td>
<td>One or more seasonal periods passed to statsmodels MSTL.</td>
</tr>
<tr>
<td><code>windows</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Optional smoother windows aligned with periods.</td>
</tr>
<tr>
<td><code>stl_kwargs</code></td>
<td>dict | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Additional statsmodels STL keyword arguments.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;MSTL&quot;,
&quot;params&quot;: {
&quot;periods&quot;: [
12,
24
]
}
}
</code></pre>
<h3 id="mvmd"><code>MVMD</code></h3>
<ul>
<li>Input mode: <code>multivariate</code></li>
<li>Maturity: <code>optional-backend</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.modes</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>K</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>4</code></td>
<td>Number of shared variational modes requested from PySDKit.</td>
</tr>
<tr>
<td><code>alpha</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>2000.0</code></td>
<td>Bandwidth penalty parameter for the MVMD backend.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Shared period hint for grouping modes.</td>
</tr>
<tr>
<td><code>fs</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1.0</code></td>
<td>Sampling frequency used by grouping heuristics.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;MVMD&quot;,
&quot;params&quot;: {
&quot;K&quot;: 4,
&quot;alpha&quot;: 2000.0,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="nbeats_interpretable"><code>NBEATS_INTERPRETABLE</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>degree_of_polynomial</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>3</code></td>
<td>Polynomial trend basis degree.</td>
</tr>
<tr>
<td><code>num_harmonics</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>8</code></td>
<td>Number of Fourier harmonics in the seasonality stack.</td>
</tr>
<tr>
<td><code>trend_blocks</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>2</code></td>
<td>Number of interpretable trend blocks.</td>
</tr>
<tr>
<td><code>seasonality_blocks</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>2</code></td>
<td>Number of interpretable seasonality blocks.</td>
</tr>
<tr>
<td><code>layers</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>6</code></td>
<td>Fully connected layers per block.</td>
</tr>
<tr>
<td><code>layer_size</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>128</code></td>
<td>Hidden width for each block.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit on the full series or prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>n_epochs</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>200</code></td>
<td>Maximum torch optimization epochs.</td>
</tr>
<tr>
<td><code>patience</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>40</code></td>
<td>Early-stopping patience.</td>
</tr>
<tr>
<td><code>restarts</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>2</code></td>
<td>Number of random restarts.</td>
</tr>
<tr>
<td><code>learning_rate</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.001</code></td>
<td>Adam learning rate.</td>
</tr>
<tr>
<td><code>weight_decay</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.0001</code></td>
<td>Adam weight decay.</td>
</tr>
<tr>
<td><code>device</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"auto"</code></td>
<td>Torch device: auto, cpu, cuda, or gpu.</td>
</tr>
<tr>
<td><code>seed</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>0</code></td>
<td>Base random seed for torch restarts.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;NBEATS_INTERPRETABLE&quot;,
&quot;params&quot;: {
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;n_epochs&quot;: 200,
&quot;num_harmonics&quot;: 8,
&quot;restarts&quot;: 2
}
}
</code></pre>
<h3 id="parsimony_block"><code>PARSIMONY_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>trend_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average trend window.</td>
</tr>
<tr>
<td><code>num_harmonics</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>2</code></td>
<td>Number of harmonic seasonal terms.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;PARSIMONY_BLOCK&quot;,
&quot;params&quot;: {
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;num_harmonics&quot;: 2,
&quot;primary_period&quot;: 12,
&quot;trend_window&quot;: 13
}
}
</code></pre>
<h3 id="robust_stl"><code>ROBUST_STL</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>period</code></td>
<td>int</td>
<td style="text-align: right;">yes</td>
<td>required</td>
<td>Seasonal period passed to robust statsmodels STL.</td>
</tr>
<tr>
<td><code>seasonal</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Odd LOESS seasonal smoother length.</td>
</tr>
<tr>
<td><code>trend</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Odd LOESS trend smoother length.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;ROBUST_STL&quot;,
&quot;params&quot;: {
&quot;period&quot;: 12
}
}
</code></pre>
<h3 id="ssa"><code>SSA</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>flagship</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.elementary</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>window</code></td>
<td>int</td>
<td style="text-align: right;">yes</td>
<td>required</td>
<td>Embedding window length for trajectory-matrix construction.</td>
</tr>
<tr>
<td><code>rank</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Number of elementary components to retain before grouping.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant seasonal period used by automatic grouping.</td>
</tr>
<tr>
<td><code>fs</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1.0</code></td>
<td>Sampling frequency used by frequency-based grouping.</td>
</tr>
<tr>
<td><code>trend_components</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit component indexes assigned to trend.</td>
</tr>
<tr>
<td><code>season_components</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Explicit component indexes assigned to season.</td>
</tr>
<tr>
<td><code>power_iterations</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>4</code></td>
<td>Fast native mode iteration count when speed_mode='fast'.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;backend&quot;: &quot;auto&quot;,
&quot;method&quot;: &quot;SSA&quot;,
&quot;params&quot;: {
&quot;primary_period&quot;: 12,
&quot;rank&quot;: 6,
&quot;window&quot;: 24
},
&quot;seed&quot;: 42,
&quot;speed_mode&quot;: &quot;exact&quot;
}
</code></pre>
<h3 id="std"><code>STD</code></h3>
<ul>
<li>Input mode: <code>channelwise</code></li>
<li>Maturity: <code>flagship</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.dispersion</code>, <code>components.seasonal_shape</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>period</code></td>
<td>int</td>
<td style="text-align: right;">yes</td>
<td>required</td>
<td>Seasonal period in samples.</td>
</tr>
<tr>
<td><code>max_period_search</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Optional search horizon when period inference is enabled.</td>
</tr>
<tr>
<td><code>eps</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1e-08</code></td>
<td>Small numerical guard for dispersion calculations.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;backend&quot;: &quot;auto&quot;,
&quot;method&quot;: &quot;STD&quot;,
&quot;params&quot;: {
&quot;period&quot;: 12
},
&quot;speed_mode&quot;: &quot;exact&quot;
}
</code></pre>
<h3 id="stdr"><code>STDR</code></h3>
<ul>
<li>Input mode: <code>channelwise</code></li>
<li>Maturity: <code>flagship</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.dispersion</code>, <code>components.seasonal_shape</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>period</code></td>
<td>int</td>
<td style="text-align: right;">yes</td>
<td>required</td>
<td>Seasonal period in samples.</td>
</tr>
<tr>
<td><code>max_period_search</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Optional search horizon when period inference is enabled.</td>
</tr>
<tr>
<td><code>eps</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1e-08</code></td>
<td>Small numerical guard for robust dispersion calculations.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;backend&quot;: &quot;auto&quot;,
&quot;method&quot;: &quot;STDR&quot;,
&quot;params&quot;: {
&quot;period&quot;: 12
},
&quot;speed_mode&quot;: &quot;exact&quot;
}
</code></pre>
<h3 id="stl"><code>STL</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>period</code></td>
<td>int</td>
<td style="text-align: right;">yes</td>
<td>required</td>
<td>Seasonal period passed to statsmodels STL.</td>
</tr>
<tr>
<td><code>seasonal</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Odd LOESS seasonal smoother length.</td>
</tr>
<tr>
<td><code>trend</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Odd LOESS trend smoother length.</td>
</tr>
<tr>
<td><code>robust</code></td>
<td>bool</td>
<td style="text-align: right;">no</td>
<td><code>false</code></td>
<td>Whether to use robust LOESS fitting.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;STL&quot;,
&quot;params&quot;: {
&quot;period&quot;: 12
}
}
</code></pre>
<h3 id="st_mtm_block"><code>ST_MTM_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>trend_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average trend window.</td>
</tr>
<tr>
<td><code>season_smooth_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Smoother applied to the periodic seasonal template.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;ST_MTM_BLOCK&quot;,
&quot;params&quot;: {
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;primary_period&quot;: 12,
&quot;season_smooth_window&quot;: 7,
&quot;trend_window&quot;: 13
}
}
</code></pre>
<h3 id="timekan_block"><code>TIMEKAN_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>trend_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average trend window.</td>
</tr>
<tr>
<td><code>num_bands</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>2</code></td>
<td>Number of dominant periods/templates to blend.</td>
</tr>
<tr>
<td><code>kan_width</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>32</code></td>
<td>KAN-inspired width used to choose harmonic capacity.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;TIMEKAN_BLOCK&quot;,
&quot;params&quot;: {
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;kan_width&quot;: 32,
&quot;num_bands&quot;: 2,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="times2d_block"><code>TIMES2D_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Dominant period hint used by neural block heuristics.</td>
</tr>
<tr>
<td><code>fit_scope</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"full"</code></td>
<td>Whether to fit templates on the full series or a prefix window.</td>
</tr>
<tr>
<td><code>train_fraction</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.6</code></td>
<td>Prefix fraction used when fit_scope='prefix'.</td>
</tr>
<tr>
<td><code>top_k_periods</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>2</code></td>
<td>Number of dominant FFT periods to retain.</td>
</tr>
<tr>
<td><code>num_harmonics</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>1</code></td>
<td>Number of harmonics per selected period.</td>
</tr>
<tr>
<td><code>trend_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Moving-average trend window.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;TIMES2D_BLOCK&quot;,
&quot;params&quot;: {
&quot;fit_scope&quot;: &quot;full&quot;,
&quot;num_harmonics&quot;: 1,
&quot;primary_period&quot;: 12,
&quot;top_k_periods&quot;: 2
}
}
</code></pre>
<h3 id="vmd"><code>VMD</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.modes</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>K</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>4</code></td>
<td>Number of variational modes.</td>
</tr>
<tr>
<td><code>alpha</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>2000.0</code></td>
<td>Bandwidth penalty parameter.</td>
</tr>
<tr>
<td><code>tau</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>0.0</code></td>
<td>Dual ascent time step.</td>
</tr>
<tr>
<td><code>DC</code></td>
<td>bool</td>
<td style="text-align: right;">no</td>
<td><code>false</code></td>
<td>Keep the first mode at zero frequency.</td>
</tr>
<tr>
<td><code>init</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>1</code></td>
<td>Initialization policy used by the VMD backend.</td>
</tr>
<tr>
<td><code>tol</code></td>
<td>float</td>
<td style="text-align: right;">no</td>
<td><code>1e-07</code></td>
<td>Convergence tolerance.</td>
</tr>
<tr>
<td><code>primary_period</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Period hint for grouping modes into season and trend.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;VMD&quot;,
&quot;params&quot;: {
&quot;K&quot;: 4,
&quot;alpha&quot;: 2000.0,
&quot;primary_period&quot;: 12
}
}
</code></pre>
<h3 id="waveform_block"><code>WAVEFORM_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>wavelet</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"db4"</code></td>
<td>PyWavelets wavelet family name.</td>
</tr>
<tr>
<td><code>level</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>3</code></td>
<td>Wavelet decomposition depth.</td>
</tr>
<tr>
<td><code>season_levels</code></td>
<td>list[int]</td>
<td style="text-align: right;">no</td>
<td><code>[1, 2]</code></td>
<td>Detail coefficient levels assigned to the seasonal component.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;WAVEFORM_BLOCK&quot;,
&quot;params&quot;: {
&quot;level&quot;: 3,
&quot;season_levels&quot;: [
1,
2
],
&quot;wavelet&quot;: &quot;sym4&quot;
}
}
</code></pre>
<h3 id="wavelet"><code>WAVELET</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>stable</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.coefficients</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>wavelet</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"db4"</code></td>
<td>PyWavelets wavelet family name.</td>
</tr>
<tr>
<td><code>level</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Decomposition depth. Defaults to PyWavelets maximum usable level.</td>
</tr>
<tr>
<td><code>trend_levels</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Detail levels assigned to trend reconstruction.</td>
</tr>
<tr>
<td><code>season_levels</code></td>
<td>list[int] | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Detail levels assigned to seasonal reconstruction.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;WAVELET&quot;,
&quot;params&quot;: {
&quot;level&quot;: 3,
&quot;wavelet&quot;: &quot;db4&quot;
}
}
</code></pre>
<h3 id="waveletmixer_block"><code>WAVELETMIXER_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>wavelet</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"sym4"</code></td>
<td>PyWavelets wavelet family name.</td>
</tr>
<tr>
<td><code>level</code></td>
<td>int</td>
<td style="text-align: right;">no</td>
<td><code>4</code></td>
<td>Wavelet decomposition depth.</td>
</tr>
<tr>
<td><code>season_levels</code></td>
<td>list[int]</td>
<td style="text-align: right;">no</td>
<td><code>[1, 2, 3]</code></td>
<td>Detail coefficient levels assigned to the seasonal component.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;WAVELETMIXER_BLOCK&quot;,
&quot;params&quot;: {
&quot;level&quot;: 3,
&quot;season_levels&quot;: [
1,
2,
3
],
&quot;wavelet&quot;: &quot;coif1&quot;
}
}
</code></pre>
<h3 id="xpatch_block"><code>XPATCH_BLOCK</code></h3>
<ul>
<li>Input mode: <code>univariate</code></li>
<li>Maturity: <code>experimental</code></li>
<li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li>
</ul>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Type</th>
<th style="text-align: right;">Required</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>ma_type</code></td>
<td>str</td>
<td style="text-align: right;">no</td>
<td><code>"ema"</code></td>
<td>Smoothing type, either 'ema' or 'dema'.</td>
</tr>
<tr>
<td><code>trend_window</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Window used to derive the EMA alpha.</td>
</tr>
<tr>
<td><code>season_smooth</code></td>
<td>int | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>Optional moving-average smoother for the seasonal residual.</td>
</tr>
<tr>
<td><code>alpha</code></td>
<td>float | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>EMA or DEMA level smoothing coefficient.</td>
</tr>
<tr>
<td><code>beta</code></td>
<td>float | None</td>
<td style="text-align: right;">no</td>
<td><code>None</code></td>
<td>DEMA slope smoothing coefficient.</td>
</tr>
</tbody>
</table>
<p>Example config:</p>
<pre><code class="language-json">{
&quot;method&quot;: &quot;XPATCH_BLOCK&quot;,
&quot;params&quot;: {
&quot;season_smooth&quot;: 7,
&quot;trend_window&quot;: 25
}
}
</code></pre></div>
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