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| <h1 id="method-references">Method References</h1> |
| <p>This page is generated from <code>MethodRegistry.list_catalog()</code> so citations, |
| upstream package links, and method metadata stay aligned.</p> |
| <p>Current package version target: <code>0.1.1</code>.</p> |
| <p>These links cover the method families and upstream packages used or compared |
| in the public DeTime workflow surface. <code>MA_BASELINE</code> is an in-package smoke |
| baseline and therefore has no separate upstream citation.</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://link.springer.com/book/10.1007/978-3-662-62436-4">Golyandina and Zhigljavsky (2020), Singular Spectrum Analysis for Time Series</a> - Primary SSA/MSSA reference used for the multivariate extension.</p> |
| <p>Related packages: |
| - <a href="https://github.com/ADSCIAN/ssalib">SSALib</a> - SSA-focused package; useful comparison point for SSA-family workflows.</p> |
| <h3 id="ssa"><code>SSA</code></h3> |
| <ul> |
| <li>Summary: Singular spectrum analysis for structured univariate decomposition.</li> |
| <li>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://link.springer.com/book/10.1007/978-3-662-62436-4">Golyandina and Zhigljavsky (2020), Singular Spectrum Analysis for Time Series</a> - Primary SSA reference; the second edition also covers multivariate SSA (MSSA).</p> |
| <p>Related packages: |
| - <a href="https://github.com/ADSCIAN/ssalib">SSALib</a> - Specialist SSA package used as an external comparison point.</p> |
| <h3 id="std"><code>STD</code></h3> |
| <ul> |
| <li>Summary: Fast seasonal-trend decomposition with dispersion-aware diagnostics.</li> |
| <li>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://doi.org/10.48550/arXiv.2204.10398">Dudek (2022), STD: A Seasonal-Trend-Dispersion Decomposition of Time Series</a> - Primary reference for STD and the robust seasonal-trend-dispersion family.</p> |
| <p>Related packages: |
| - none declared</p> |
| <h3 id="stdr"><code>STDR</code></h3> |
| <ul> |
| <li>Summary: Robust seasonal-trend decomposition for noisier periodic signals.</li> |
| <li>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://doi.org/10.48550/arXiv.2204.10398">Dudek (2022), STD: A Seasonal-Trend-Dispersion Decomposition of Time Series</a> - Primary reference for STD and the robust seasonal-trend-dispersion family.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: PyEMD</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://pyemd.readthedocs.io/en/latest/ceemdan.html">Torres et al. (2011), A complete ensemble empirical mode decomposition with adaptive noise</a> - PyEMD CEEMDAN docs cite the original ICASSP 2011 paper. |
| - <a href="https://pyemd.readthedocs.io/en/latest/ceemdan.html">Colominas, Schlotthauer, and Torres (2014), Improved complete ensemble EMD: A suitable tool for biomedical signal processing</a> - Improved CEEMDAN variant adopted by the PyEMD implementation used by DeTime.</p> |
| <p>Related packages: |
| - <a href="https://github.com/laszukdawid/PyEMD">PyEMD</a> - Upstream Python package wrapped by DeTime for EMD-family methods.</p> |
| <h3 id="emd"><code>EMD</code></h3> |
| <ul> |
| <li>Summary: Empirical mode decomposition under the DeTime result contract.</li> |
| <li>Optional/runtime dependencies: PyEMD</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://doi.org/10.1098/rspa.1998.0193">Huang et al. (1998), The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis</a> - Primary empirical mode decomposition reference.</p> |
| <p>Related packages: |
| - <a href="https://github.com/laszukdawid/PyEMD">PyEMD</a> - Upstream Python package wrapped by DeTime for EMD-family methods.</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - none declared</p> |
| <p>Related packages: |
| - none declared</p> |
| <h3 id="mstl"><code>MSTL</code></h3> |
| <ul> |
| <li>Summary: Statsmodels MSTL wrapped into the DeTime workflow surface.</li> |
| <li>Optional/runtime dependencies: statsmodels</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2107.13462">Bandara, Hyndman, and Bergmeir (2021), MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns</a> - Primary MSTL reference used by the statsmodels implementation.</p> |
| <p>Related packages: |
| - <a href="https://www.statsmodels.org/">statsmodels</a> - Official project site for the upstream MSTL implementation.</p> |
| <h3 id="robust_stl"><code>ROBUST_STL</code></h3> |
| <ul> |
| <li>Summary: Robust STL-style decomposition wrapped for reproducible workflows.</li> |
| <li>Optional/runtime dependencies: statsmodels</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html">Cleveland et al. (1990), STL: A Seasonal-Trend Decomposition Procedure Based on LOESS</a> - Robust STL in DeTime builds on the same STL literature and upstream implementation family.</p> |
| <p>Related packages: |
| - <a href="https://www.statsmodels.org/">statsmodels</a> - Official project site for the upstream STL implementation family.</p> |
| <h3 id="stl"><code>STL</code></h3> |
| <ul> |
| <li>Summary: Classical STL wrapped into the DeTime workflow contract.</li> |
| <li>Optional/runtime dependencies: statsmodels</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html">Cleveland et al. (1990), STL: A Seasonal-Trend Decomposition Procedure Based on LOESS</a> - Statsmodels STL docs cite the original Journal of Official Statistics paper.</p> |
| <p>Related packages: |
| - <a href="https://www.statsmodels.org/">statsmodels</a> - Official project site for the upstream STL implementation.</p> |
| <h3 id="vmd"><code>VMD</code></h3> |
| <ul> |
| <li>Summary: Variational mode decomposition integrated into the common workflow layer.</li> |
| <li>Optional/runtime dependencies: vmdpy, sktime</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://doi.org/10.1109/TSP.2013.2288675">Dragomiretskiy and Zosso (2014), Variational Mode Decomposition</a> - Primary variational mode decomposition reference.</p> |
| <p>Related packages: |
| - <a href="https://www.sktime.net/en/stable/">sktime</a> - Current maintained ecosystem for <code>vmdpy</code>, which the archived project directs users toward. |
| - <a href="https://github.com/vrcarva/vmdpy">vmdpy</a> - Archived Python VMD package used by the current DeTime wrapper.</p> |
| <h3 id="wavelet"><code>WAVELET</code></h3> |
| <ul> |
| <li>Summary: Wavelet-based decomposition exposed through the common output contract.</li> |
| <li>Optional/runtime dependencies: PyWavelets</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://ieeexplore.ieee.org/document/192463">Mallat (1989), A theory for multiresolution signal decomposition: the wavelet representation</a> - Foundational wavelet multiresolution reference. |
| - <a href="https://doi.org/10.21105/joss.01237">Lee et al. (2019), PyWavelets: A Python package for wavelet analysis</a> - Package citation for the upstream wavelet implementation used by DeTime.</p> |
| <p>Related packages: |
| - <a href="https://pywavelets.readthedocs.io/en/latest/">PyWavelets</a> - Official documentation for the upstream wavelet package.</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>Optional/runtime dependencies: PySDKit</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://doi.org/10.1098/rspa.2009.0502">Rehman and Mandic (2010), Multivariate empirical mode decomposition</a> - Primary MEMD reference for the multivariate EMD extension.</p> |
| <p>Related packages: |
| - <a href="https://pysdkit.readthedocs.io/en/latest/">PySDKit</a> - Optional multivariate backend used by DeTime for MEMD.</p> |
| <h3 id="mvmd"><code>MVMD</code></h3> |
| <ul> |
| <li>Summary: Optional multivariate VMD backend for shared frequency structure.</li> |
| <li>Optional/runtime dependencies: PySDKit</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/1907.04509">Rehman and Aftab (2019), Multivariate Variational Mode Decomposition</a> - Primary MVMD reference for the multivariate VMD extension.</p> |
| <p>Related packages: |
| - <a href="https://pysdkit.readthedocs.io/en/latest/">PySDKit</a> - Optional multivariate backend used by DeTime for MVMD.</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2406.03751">Hu et al. (2024), Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting</a> - Source framework for adaptive multiscale decomposition.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://proceedings.neurips.cc/paper_files/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html">Wu et al. (2021), Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting</a> - Source architecture for the moving-average decomposition block exposed by AUTOFORMER_BLOCK.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2308.13797">Wang et al. (2023), DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series</a> - Source model for decomposition-based explainable LSTM effects.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://ojs.aaai.org/index.php/AAAI/article/view/26317">Zeng et al. (2023), Are Transformers Effective for Time Series Forecasting?</a> - Introduces the LTSF-Linear family, including the DLinear decomposition-based linear model.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2501.15125">Liu (2025), FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts</a> - Source architecture for frequency decomposition mixture-of-experts forecasting.</p> |
| <p>Related packages: |
| - none declared</p> |
| <h3 id="gabor_cluster"><code>GABOR_CLUSTER</code></h3> |
| <ul> |
| <li>Summary: Experimental clustering-based decomposition path.</li> |
| <li>Optional/runtime dependencies: faiss</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://www.rctn.org/w/images/b/b6/Gabor.pdf">Gabor (1946), Theory of Communication</a> - Historical reference for the Gabor time-frequency representation family. |
| - <a href="https://arxiv.org/abs/2401.08281">Douze et al. (2024), The Faiss library</a> - Reference for the similarity-search backend used by the experimental clustering path.</p> |
| <p>Related packages: |
| - <a href="https://github.com/facebookresearch/faiss">Faiss</a> - Vector similarity search library required by the experimental clustering backend.</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://ojs.aaai.org/index.php/AAAI/article/view/25845">Cao et al. (2023), InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting</a> - Source architecture for evolutionary seasonal-trend decomposition in a transformer forecaster.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2402.12694">Yu et al. (2024), Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling</a> - Introduces LEDDAM, the learnable decomposition and dual-attention module.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://proceedings.neurips.cc/paper_files/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html">Wu et al. (2021), Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting</a> - Primary source for treating moving-average series decomposition as an internal neural forecasting block. |
| - <a href="https://ojs.aaai.org/index.php/AAAI/article/view/26317">Zeng et al. (2023), Are Transformers Effective for Time Series Forecasting?</a> - Uses decomposition-based linear forecasting as a simple long-term forecasting baseline.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: torch</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://openreview.net/forum?id=r1ecqn4YwB">Oreshkin et al. (2020), N-BEATS: Neural basis expansion analysis for interpretable time series forecasting</a> - Source for interpretable trend and seasonality basis stacks.</p> |
| <p>Related packages: |
| - none declared</p> |
| <h3 id="parsimony_block"><code>PARSIMONY_BLOCK</code></h3> |
| <ul> |
| <li>Summary: Parsimony-inspired trend head with compact harmonic seasonal projection.</li> |
| <li>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2401.11929">Deng et al. (2024), Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting</a> - Source paper for parameter-efficient decomposition in long-term forecasting.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2507.00013">Seo and Lim (2025), ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting</a> - Source method for seasonal-trend masked time-series modeling.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2502.06910">Huang et al. (2025), TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting</a> - Source method for KAN-based frequency decomposition learning.</p> |
| <p>Related packages: |
| - none declared</p> |
| <h3 id="times2d_block"><code>TIMES2D_BLOCK</code></h3> |
| <ul> |
| <li>Summary: Times2D-inspired multi-period harmonic decomposition head.</li> |
| <li>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2504.00118">Nematirad, Pahwa, and Natarajan (2025), Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting</a> - Source method for multi-period decomposition and 2D time-series mapping.</p> |
| <p>Related packages: |
| - none declared</p> |
| <h3 id="waveform_block"><code>WAVEFORM_BLOCK</code></h3> |
| <ul> |
| <li>Summary: WaveForM-inspired wavelet multiresolution decomposition head.</li> |
| <li>Optional/runtime dependencies: PyWavelets</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://ojs.aaai.org/index.php/AAAI/article/view/26276">Yang et al. (2023), WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series</a> - Source architecture for graph-enhanced wavelet learning.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: PyWavelets</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://ojs.aaai.org/index.php/AAAI/article/view/34434">Zhang et al. (2025), WaveletMixer: A Multi-Resolution Wavelets Based MLP-Mixer for Multivariate Long-Term Time Series Forecasting</a> - Source method for multi-resolution wavelet mixer forecasting.</p> |
| <p>Related packages: |
| - none declared</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>Optional/runtime dependencies: none</li> |
| </ul> |
| <p>Primary references: |
| - <a href="https://arxiv.org/abs/2412.17323">Stitsyuk and Choi (2024), xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition</a> - Source architecture for exponential seasonal-trend decomposition.</p> |
| <p>Related packages: |
| - none declared</p></div> |
| </div> |
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