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| <h1 id="method-cards">Method Cards</h1> |
| <p>This page is generated from <code>MethodRegistry.list_catalog()</code> so the human-facing |
| method cards stay aligned with the machine-facing catalog contract.</p> |
| <p>Current package version target: <code>0.1.1</code>.</p> |
| <p>Source citations and official upstream package links are collected in |
| <a href="../method-references/">Method References</a>.</p> |
| <p>This page intentionally keeps cards compact. Use |
| <a href="../method-matrix/">Method Matrix</a> for table comparison and |
| <a href="../config-reference/">Config Reference</a> for full parameter semantics.</p> |
| <p>The <code>tsdecomp</code> top-level alias remains compatibility-only through <code>0.1.x</code> and is |
| not the canonical surface for any method listed below.</p> |
| <h2 id="flagship-methods">Flagship methods</h2> |
| <h3 id="mssa"><code>MSSA</code></h3> |
| <ul> |
| <li>Summary: Multivariate SSA for shared-structure decomposition across channels.</li> |
| <li>Use when: multivariate component recovery; shared seasonal structure across channels</li> |
| <li>Avoid when: single-series workflows where a univariate flagship method is sufficient; very short series that cannot support a sensible window length</li> |
| <li>Key params: <code>window</code> (required), <code>rank</code> (null), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>multivariate</code> input, <code>native-backed</code> implementation, maturity <code>flagship</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.elementary</code></li> |
| <li>References: <a href="../method-references/#mssa">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#mssa">Config Reference</a> for the full parameter table.</p> |
| <h3 id="ssa"><code>SSA</code></h3> |
| <ul> |
| <li>Summary: Singular spectrum analysis for structured univariate decomposition.</li> |
| <li>Use when: accuracy-first univariate decomposition; component recovery</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; very short series that cannot support a sensible window length</li> |
| <li>Key params: <code>window</code> (required), <code>rank</code> (null), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>native-backed</code> implementation, maturity <code>flagship</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.elementary</code></li> |
| <li>References: <a href="../method-references/#ssa">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#ssa">Config Reference</a> for the full parameter table.</p> |
| <h3 id="std"><code>STD</code></h3> |
| <ul> |
| <li>Summary: Fast seasonal-trend decomposition with dispersion-aware diagnostics.</li> |
| <li>Use when: fast seasonal-trend baselines; channelwise multivariate workflows</li> |
| <li>Avoid when: problems that require one shared latent model across channels; series where the dominant period is unknown and cannot be inferred reliably</li> |
| <li>Key params: <code>period</code> (required)</li> |
| <li>Input/backend: <code>channelwise</code> input, <code>native-backed</code> implementation, maturity <code>flagship</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.dispersion</code>, <code>components.seasonal_shape</code></li> |
| <li>References: <a href="../method-references/#std">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#std">Config Reference</a> for the full parameter table.</p> |
| <h3 id="stdr"><code>STDR</code></h3> |
| <ul> |
| <li>Summary: Robust seasonal-trend decomposition for noisier periodic signals.</li> |
| <li>Use when: robust seasonal-trend decomposition; channelwise multivariate workflows</li> |
| <li>Avoid when: problems that require one shared latent model across channels; series where the dominant period is unknown and cannot be inferred reliably</li> |
| <li>Key params: <code>period</code> (required)</li> |
| <li>Input/backend: <code>channelwise</code> input, <code>native-backed</code> implementation, maturity <code>flagship</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.dispersion</code>, <code>components.seasonal_shape</code></li> |
| <li>References: <a href="../method-references/#stdr">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#stdr">Config Reference</a> for the full parameter table.</p> |
| <h2 id="stable-wrappers-and-retained-methods">Stable wrappers and retained methods</h2> |
| <h3 id="ceemdan"><code>CEEMDAN</code></h3> |
| <ul> |
| <li>Summary: Noise-assisted EMD variant for more stable IMF extraction.</li> |
| <li>Use when: noise-assisted EMD workflows; adaptive decomposition with improved IMF stability</li> |
| <li>Avoid when: shared-model multivariate decomposition problems</li> |
| <li>Key params: <code>trials</code> (50), <code>noise_width</code> (0.05), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: PyEMD</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.imfs</code></li> |
| <li>References: <a href="../method-references/#ceemdan">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#ceemdan">Config Reference</a> for the full parameter table.</p> |
| <h3 id="emd"><code>EMD</code></h3> |
| <ul> |
| <li>Summary: Empirical mode decomposition under the DeTime result contract.</li> |
| <li>Use when: adaptive decomposition of nonlinear signals; IMF-oriented exploratory analysis</li> |
| <li>Avoid when: shared-model multivariate decomposition problems</li> |
| <li>Key params: <code>n_imfs</code> (null), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: PyEMD</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.imfs</code></li> |
| <li>References: <a href="../method-references/#emd">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#emd">Config Reference</a> for the full parameter table.</p> |
| <h3 id="ma_baseline"><code>MA_BASELINE</code></h3> |
| <ul> |
| <li>Summary: Simple moving-average baseline for smoke tests and lightweight workflows.</li> |
| <li>Use when: sanity checks; lightweight baseline decomposition</li> |
| <li>Avoid when: shared-model multivariate decomposition problems</li> |
| <li>Key params: <code>trend_window</code> (7), <code>season_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>native-backed</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code></li> |
| <li>References: <a href="../method-references/#ma_baseline">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#ma_baseline">Config Reference</a> for the full parameter table.</p> |
| <h3 id="mstl"><code>MSTL</code></h3> |
| <ul> |
| <li>Summary: Statsmodels MSTL wrapped into the DeTime workflow surface.</li> |
| <li>Use when: multiple seasonalities in univariate data; classical decomposition baselines</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; series where the dominant period is unknown and cannot be inferred reliably</li> |
| <li>Key params: <code>periods</code> (required)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>wrapper</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: statsmodels</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.seasonal_terms</code></li> |
| <li>References: <a href="../method-references/#mstl">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#mstl">Config Reference</a> for the full parameter table.</p> |
| <h3 id="robust_stl"><code>ROBUST_STL</code></h3> |
| <ul> |
| <li>Summary: Robust STL-style decomposition wrapped for reproducible workflows.</li> |
| <li>Use when: outlier-prone seasonal-trend baselines; classical robust decomposition</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; series where the dominant period is unknown and cannot be inferred reliably</li> |
| <li>Key params: <code>period</code> (required)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>wrapper</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: statsmodels</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code></li> |
| <li>References: <a href="../method-references/#robust_stl">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#robust_stl">Config Reference</a> for the full parameter table.</p> |
| <h3 id="stl"><code>STL</code></h3> |
| <ul> |
| <li>Summary: Classical STL wrapped into the DeTime workflow contract.</li> |
| <li>Use when: classical seasonal-trend baselines; statsmodels-compatible workflows</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; series where the dominant period is unknown and cannot be inferred reliably</li> |
| <li>Key params: <code>period</code> (required)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>wrapper</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: statsmodels</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code></li> |
| <li>References: <a href="../method-references/#stl">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#stl">Config Reference</a> for the full parameter table.</p> |
| <h3 id="vmd"><code>VMD</code></h3> |
| <ul> |
| <li>Summary: Variational mode decomposition integrated into the common workflow layer.</li> |
| <li>Use when: band-limited mode separation; frequency-structured univariate workflows</li> |
| <li>Avoid when: shared-model multivariate decomposition problems</li> |
| <li>Key params: <code>K</code> (4), <code>alpha</code> (2000.0), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>native-backed</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: vmdpy, sktime</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.modes</code></li> |
| <li>References: <a href="../method-references/#vmd">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#vmd">Config Reference</a> for the full parameter table.</p> |
| <h3 id="wavelet"><code>WAVELET</code></h3> |
| <ul> |
| <li>Summary: Wavelet-based decomposition exposed through the common output contract.</li> |
| <li>Use when: multiscale exploratory analysis; wavelet-style trend and detail separation</li> |
| <li>Avoid when: shared-model multivariate decomposition problems</li> |
| <li>Key params: <code>wavelet</code> ("db4"), <code>level</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>wrapper</code> implementation, maturity <code>stable</code></li> |
| <li>Optional dependencies: PyWavelets</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.coefficients</code></li> |
| <li>References: <a href="../method-references/#wavelet">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#wavelet">Config Reference</a> for the full parameter table.</p> |
| <h2 id="optional-backend-methods">Optional backend methods</h2> |
| <h3 id="memd"><code>MEMD</code></h3> |
| <ul> |
| <li>Summary: Optional multivariate EMD backend for shared oscillatory structure.</li> |
| <li>Use when: multivariate adaptive decomposition; shared oscillatory modes across channels</li> |
| <li>Avoid when: single-series workflows where a univariate flagship method is sufficient; environments where optional backend dependencies cannot be installed</li> |
| <li>Key params: <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>multivariate</code> input, <code>optional-backend</code> implementation, maturity <code>optional-backend</code></li> |
| <li>Optional dependencies: PySDKit</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.imfs</code></li> |
| <li>References: <a href="../method-references/#memd">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#memd">Config Reference</a> for the full parameter table.</p> |
| <h3 id="mvmd"><code>MVMD</code></h3> |
| <ul> |
| <li>Summary: Optional multivariate VMD backend for shared frequency structure.</li> |
| <li>Use when: multivariate variational decomposition; shared frequency structure across channels</li> |
| <li>Avoid when: single-series workflows where a univariate flagship method is sufficient; environments where optional backend dependencies cannot be installed</li> |
| <li>Key params: <code>K</code> (4), <code>alpha</code> (2000.0), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>multivariate</code> input, <code>optional-backend</code> implementation, maturity <code>optional-backend</code></li> |
| <li>Optional dependencies: PySDKit</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.modes</code></li> |
| <li>References: <a href="../method-references/#mvmd">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#mvmd">Config Reference</a> for the full parameter table.</p> |
| <h2 id="experimental-methods">Experimental methods</h2> |
| <h3 id="amd_block"><code>AMD_BLOCK</code></h3> |
| <ul> |
| <li>Summary: AMD-inspired multiscale smoothing head with periodic-template seasonal reconstruction.</li> |
| <li>Use when: multiscale neural decomposition comparisons; seasonal signals where multiple smoothing scales are informative</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>multiscale_windows</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#amd_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#amd_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="autoformer_block"><code>AUTOFORMER_BLOCK</code></h3> |
| <ul> |
| <li>Summary: Standalone moving-average decomposition head extracted from the Autoformer architecture.</li> |
| <li>Use when: neural-architecture-inspired seasonal-trend baselines; Autoformer-style decomposition ablations</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>moving_avg</code> (null), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.moving_mean</code></li> |
| <li>References: <a href="../method-references/#autoformer_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#autoformer_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="delelstm_block"><code>DELELSTM_BLOCK</code></h3> |
| <ul> |
| <li>Summary: DeLELSTM-inspired Holt trend plus periodic-template seasonal decomposition head.</li> |
| <li>Use when: LSTM decomposition-head ablations; signals with smooth level and slope structure</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>alpha</code> (0.4), <code>beta</code> (0.2)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#delelstm_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#delelstm_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="dlinear_block"><code>DLINEAR_BLOCK</code></h3> |
| <ul> |
| <li>Summary: Standalone moving-average decomposition head extracted from DLinear-style forecasting blocks.</li> |
| <li>Use when: DLinear-style trend/season split baselines; fast neural decomposition head comparisons</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>moving_avg</code> (null), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.moving_mean</code></li> |
| <li>References: <a href="../method-references/#dlinear_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#dlinear_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="freqmoe_block"><code>FREQMOE_BLOCK</code></h3> |
| <ul> |
| <li>Summary: FreqMoE-inspired frequency mixture head for trend plus multi-band seasonal reconstruction.</li> |
| <li>Use when: frequency-mixture neural head ablations; multi-band seasonal decomposition experiments</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>trend_window</code> (null), <code>num_bands</code> (4)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#freqmoe_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#freqmoe_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="gabor_cluster"><code>GABOR_CLUSTER</code></h3> |
| <ul> |
| <li>Summary: Experimental clustering-based decomposition path.</li> |
| <li>Use when: research prototypes; exploratory clustering-style decomposition</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>model</code> (null), <code>model_path</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>native-backed</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: faiss</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.clusters</code></li> |
| <li>References: <a href="../method-references/#gabor_cluster">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#gabor_cluster">Config Reference</a> for the full parameter table.</p> |
| <h3 id="inparformer_block"><code>INPARFORMER_BLOCK</code></h3> |
| <ul> |
| <li>Summary: InParformer-inspired moving-average trend plus periodic-template seasonal decomposition head.</li> |
| <li>Use when: periodic-template neural decomposition baselines; prefix/full-scope ablation experiments</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>trend_window</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#inparformer_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#inparformer_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="leddam_block"><code>LEDDAM_BLOCK</code></h3> |
| <ul> |
| <li>Summary: LEDDAM LD smoothing block exposed as a Gaussian-kernel decomposition operator.</li> |
| <li>Use when: LEDDAM-style decomposition ablations; kernel smoothing neural head comparisons</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>kernel_size</code> (25), <code>sigma</code> (1.0)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.ld_trend</code>, <code>components.kernel</code></li> |
| <li>References: <a href="../method-references/#leddam_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#leddam_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="moving_average_decomposition_block"><code>MOVING_AVERAGE_DECOMPOSITION_BLOCK</code></h3> |
| <ul> |
| <li>Summary: Generic neural forecasting moving-average decomposition block exposed as a DeTime method.</li> |
| <li>Use when: generic decomposition-block smoke tests; fast moving-average neural head baselines</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>moving_avg</code> (null), <code>primary_period</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.moving_mean</code></li> |
| <li>References: <a href="../method-references/#moving_average_decomposition_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#moving_average_decomposition_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="nbeats_interpretable"><code>NBEATS_INTERPRETABLE</code></h3> |
| <ul> |
| <li>Summary: Torch-backed interpretable N-BEATS trend and seasonality stacks used as a learned decomposition prior.</li> |
| <li>Use when: learned-basis decomposition experiments; N-BEATS interpretable-stack ablations</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>degree_of_polynomial</code> (3), <code>num_harmonics</code> (8), <code>fit_scope</code> ("full"), <code>n_epochs</code> (200)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: torch</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#nbeats_interpretable">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#nbeats_interpretable">Config Reference</a> for the full parameter table.</p> |
| <h3 id="parsimony_block"><code>PARSIMONY_BLOCK</code></h3> |
| <ul> |
| <li>Summary: Parsimony-inspired trend head with compact harmonic seasonal projection.</li> |
| <li>Use when: compact harmonic decomposition baselines; low-parameter neural head comparisons</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>trend_window</code> (null), <code>num_harmonics</code> (2)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#parsimony_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#parsimony_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="st_mtm_block"><code>ST_MTM_BLOCK</code></h3> |
| <ul> |
| <li>Summary: ST-MTM-inspired smoothing head combining trend smoothing and smoothed periodic seasonality.</li> |
| <li>Use when: seasonal-trend pretraining block ablations; smooth periodic decomposition baselines</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>trend_window</code> (null), <code>season_smooth_window</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#st_mtm_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#st_mtm_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="timekan_block"><code>TIMEKAN_BLOCK</code></h3> |
| <ul> |
| <li>Summary: TimeKAN-inspired decomposition head blending template and harmonic seasonal estimates.</li> |
| <li>Use when: KAN-inspired neural decomposition ablations; frequency-template hybrid seasonal baselines</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>trend_window</code> (null), <code>num_bands</code> (2)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#timekan_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#timekan_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="times2d_block"><code>TIMES2D_BLOCK</code></h3> |
| <ul> |
| <li>Summary: Times2D-inspired multi-period harmonic decomposition head.</li> |
| <li>Use when: multi-period neural decomposition baselines; FFT-selected seasonal period comparisons</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>primary_period</code> (null), <code>fit_scope</code> ("full"), <code>top_k_periods</code> (2), <code>num_harmonics</code> (1), <code>trend_window</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#times2d_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#times2d_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="waveform_block"><code>WAVEFORM_BLOCK</code></h3> |
| <ul> |
| <li>Summary: WaveForM-inspired wavelet multiresolution decomposition head.</li> |
| <li>Use when: wavelet neural-head ablations; multiresolution trend/detail comparisons</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>wavelet</code> ("db4"), <code>level</code> (3), <code>season_levels</code> ([1, 2])</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: PyWavelets</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#waveform_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#waveform_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="waveletmixer_block"><code>WAVELETMIXER_BLOCK</code></h3> |
| <ul> |
| <li>Summary: WaveletMixer-inspired multiresolution decomposition head using mixed wavelet detail levels.</li> |
| <li>Use when: wavelet-mixer neural decomposition baselines; multi-level detail seasonal reconstruction</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>wavelet</code> ("sym4"), <code>level</code> (4), <code>season_levels</code> ([1, 2, 3])</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: PyWavelets</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#waveletmixer_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#waveletmixer_block">Config Reference</a> for the full parameter table.</p> |
| <h3 id="xpatch_block"><code>XPATCH_BLOCK</code></h3> |
| <ul> |
| <li>Summary: xPatch-inspired exponential smoothing head for standalone trend and local-season decomposition.</li> |
| <li>Use when: exponential smoothing neural head comparisons; fast local seasonal-trend decomposition</li> |
| <li>Avoid when: shared-model multivariate decomposition problems; first-pass baselines or high-trust production workflows</li> |
| <li>Key params: <code>ma_type</code> ("ema"), <code>trend_window</code> (null), <code>season_smooth</code> (null)</li> |
| <li>Input/backend: <code>univariate</code> input, <code>python</code> implementation, maturity <code>experimental</code></li> |
| <li>Optional dependencies: none</li> |
| <li>Output components: <code>trend</code>, <code>season</code>, <code>residual</code>, <code>components.trend</code>, <code>components.season</code></li> |
| <li>References: <a href="../method-references/#xpatch_block">Method References</a></li> |
| </ul> |
| <p>See <a href="../config-reference/#xpatch_block">Config Reference</a> for the full parameter table.</p></div> |
| </div> |
| </div> |
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