{
  "recommendations": [
    {
      "metadata": {
        "assumptions": [
          "expects a 2D array with at least two aligned channels",
          "works best when window and rank reflect the dominant temporal structure",
          "MSSA should be evaluated against residual diagnostics rather than used as a black box"
        ],
        "dependency_tier": "core",
        "example_config": {
          "backend": "auto",
          "channel_names": [
            "channel_a",
            "channel_b",
            "channel_c"
          ],
          "method": "MSSA",
          "params": {
            "primary_period": 12,
            "rank": 6,
            "window": 24
          },
          "speed_mode": "exact"
        },
        "family": "SSA",
        "implementation": "native-backed",
        "input_mode": "multivariate",
        "maturity": "flagship",
        "min_length": 24,
        "multivariate_support": "shared-model",
        "name": "MSSA",
        "native_backed": true,
        "not_recommended_for": [
          "single-series workflows where a univariate flagship method is sufficient",
          "very short series that cannot support a sensible window length"
        ],
        "optional_dependencies": [],
        "output_components": [
          "trend",
          "season",
          "residual",
          "components.elementary"
        ],
        "package_links": [
          {
            "note": "SSA-focused package; useful comparison point for SSA-family workflows.",
            "title": "SSALib",
            "url": "https://github.com/ADSCIAN/ssalib"
          }
        ],
        "parameter_docs": [
          {
            "common": true,
            "default": null,
            "description": "Shared embedding window length for aligned channels.",
            "name": "window",
            "required": true,
            "type": "int"
          },
          {
            "common": true,
            "default": null,
            "description": "Number of shared elementary components to retain.",
            "name": "rank",
            "required": false,
            "type": "int | None"
          },
          {
            "common": true,
            "default": null,
            "description": "Dominant shared period used by automatic grouping.",
            "name": "primary_period",
            "required": false,
            "type": "int | None"
          },
          {
            "common": false,
            "default": 1.0,
            "description": "Sampling frequency used by frequency-based grouping.",
            "name": "fs",
            "required": false,
            "type": "float"
          },
          {
            "common": false,
            "default": null,
            "description": "Explicit component indexes assigned to trend.",
            "name": "trend_components",
            "required": false,
            "type": "list[int] | None"
          },
          {
            "common": false,
            "default": null,
            "description": "Explicit component indexes assigned to season.",
            "name": "season_components",
            "required": false,
            "type": "list[int] | None"
          }
        ],
        "recommended_for": [
          "multivariate component recovery",
          "shared seasonal structure across channels",
          "accuracy-first multivariate workflows"
        ],
        "references": [
          {
            "note": "Primary SSA/MSSA reference used for the multivariate extension.",
            "title": "Golyandina and Zhigljavsky (2020), Singular Spectrum Analysis for Time Series",
            "url": "https://link.springer.com/book/10.1007/978-3-662-62436-4"
          }
        ],
        "summary": "Multivariate SSA for shared-structure decomposition across channels.",
        "typical_failure_modes": [
          "too few channels for MSSA",
          "window or rank too small for the shared structure"
        ]
      },
      "method": "MSSA",
      "rank": 1,
      "reason_codes": [
        "maturity:flagship",
        "native_backed",
        "shared_multivariate",
        "accuracy_shortlist",
        "multivariate_accuracy_bonus",
        "long_series_native"
      ],
      "score": 15.75,
      "summary": "Multivariate SSA for shared-structure decomposition across channels."
    },
    {
      "metadata": {
        "assumptions": [
          "treats each channel independently under one shared method surface",
          "works best when one seasonal period or block structure is reasonably stable",
          "STDR should be evaluated against residual diagnostics rather than used as a black box"
        ],
        "dependency_tier": "core",
        "example_config": {
          "backend": "auto",
          "method": "STDR",
          "params": {
            "period": 12
          },
          "speed_mode": "exact"
        },
        "family": "SeasonalTrend",
        "implementation": "native-backed",
        "input_mode": "channelwise",
        "maturity": "flagship",
        "min_length": 8,
        "multivariate_support": "channelwise",
        "name": "STDR",
        "native_backed": true,
        "not_recommended_for": [
          "problems that require one shared latent model across channels",
          "series where the dominant period is unknown and cannot be inferred reliably"
        ],
        "optional_dependencies": [],
        "output_components": [
          "trend",
          "season",
          "residual",
          "components.dispersion",
          "components.seasonal_shape"
        ],
        "package_links": [],
        "parameter_docs": [
          {
            "common": true,
            "default": null,
            "description": "Seasonal period in samples.",
            "name": "period",
            "required": true,
            "type": "int"
          },
          {
            "common": false,
            "default": null,
            "description": "Optional search horizon when period inference is enabled.",
            "name": "max_period_search",
            "required": false,
            "type": "int | None"
          },
          {
            "common": false,
            "default": 1e-08,
            "description": "Small numerical guard for robust dispersion calculations.",
            "name": "eps",
            "required": false,
            "type": "float"
          }
        ],
        "recommended_for": [
          "robust seasonal-trend decomposition",
          "channelwise multivariate workflows",
          "native-backed seasonal structure recovery"
        ],
        "references": [
          {
            "note": "Primary reference for STD and the robust seasonal-trend-dispersion family.",
            "title": "Dudek (2022), STD: A Seasonal-Trend-Dispersion Decomposition of Time Series",
            "url": "https://doi.org/10.48550/arXiv.2204.10398"
          }
        ],
        "summary": "Robust seasonal-trend decomposition for noisier periodic signals.",
        "typical_failure_modes": [
          "period omitted or mis-specified",
          "heavy structural breaks that violate shared seasonal assumptions"
        ]
      },
      "method": "STDR",
      "rank": 2,
      "reason_codes": [
        "maturity:flagship",
        "native_backed",
        "channelwise_multivariate",
        "accuracy_shortlist",
        "long_series_native"
      ],
      "score": 11.75,
      "summary": "Robust seasonal-trend decomposition for noisier periodic signals."
    },
    {
      "metadata": {
        "assumptions": [
          "treats each channel independently under one shared method surface",
          "works best when one seasonal period or block structure is reasonably stable",
          "STD should be evaluated against residual diagnostics rather than used as a black box"
        ],
        "dependency_tier": "core",
        "example_config": {
          "backend": "auto",
          "method": "STD",
          "params": {
            "period": 12
          },
          "speed_mode": "exact"
        },
        "family": "SeasonalTrend",
        "implementation": "native-backed",
        "input_mode": "channelwise",
        "maturity": "flagship",
        "min_length": 8,
        "multivariate_support": "channelwise",
        "name": "STD",
        "native_backed": true,
        "not_recommended_for": [
          "problems that require one shared latent model across channels",
          "series where the dominant period is unknown and cannot be inferred reliably"
        ],
        "optional_dependencies": [],
        "output_components": [
          "trend",
          "season",
          "residual",
          "components.dispersion",
          "components.seasonal_shape"
        ],
        "package_links": [],
        "parameter_docs": [
          {
            "common": true,
            "default": null,
            "description": "Seasonal period in samples.",
            "name": "period",
            "required": true,
            "type": "int"
          },
          {
            "common": false,
            "default": null,
            "description": "Optional search horizon when period inference is enabled.",
            "name": "max_period_search",
            "required": false,
            "type": "int | None"
          },
          {
            "common": false,
            "default": 1e-08,
            "description": "Small numerical guard for dispersion calculations.",
            "name": "eps",
            "required": false,
            "type": "float"
          }
        ],
        "recommended_for": [
          "fast seasonal-trend baselines",
          "channelwise multivariate workflows",
          "native-backed production paths"
        ],
        "references": [
          {
            "note": "Primary reference for STD and the robust seasonal-trend-dispersion family.",
            "title": "Dudek (2022), STD: A Seasonal-Trend-Dispersion Decomposition of Time Series",
            "url": "https://doi.org/10.48550/arXiv.2204.10398"
          }
        ],
        "summary": "Fast seasonal-trend decomposition with dispersion-aware diagnostics.",
        "typical_failure_modes": [
          "period omitted or mis-specified",
          "shared seasonal structure changing too quickly across cycles"
        ]
      },
      "method": "STD",
      "rank": 3,
      "reason_codes": [
        "maturity:flagship",
        "native_backed",
        "channelwise_multivariate",
        "long_series_native"
      ],
      "score": 9.25,
      "summary": "Fast seasonal-trend decomposition with dispersion-aware diagnostics."
    }
  ],
  "rejected_methods": {
    "AMD_BLOCK": "univariate_only",
    "AUTOFORMER_BLOCK": "univariate_only",
    "CEEMDAN": "univariate_only",
    "DELELSTM_BLOCK": "univariate_only",
    "DLINEAR_BLOCK": "univariate_only",
    "EMD": "univariate_only",
    "FREQMOE_BLOCK": "univariate_only",
    "GABOR_CLUSTER": "univariate_only",
    "INPARFORMER_BLOCK": "univariate_only",
    "LEDDAM_BLOCK": "univariate_only",
    "MA_BASELINE": "univariate_only",
    "MEMD": "optional_backend_disabled",
    "MOVING_AVERAGE_DECOMPOSITION_BLOCK": "univariate_only",
    "MSTL": "univariate_only",
    "MVMD": "optional_backend_disabled",
    "NBEATS_INTERPRETABLE": "univariate_only",
    "PARSIMONY_BLOCK": "univariate_only",
    "ROBUST_STL": "univariate_only",
    "SSA": "univariate_only",
    "STL": "univariate_only",
    "ST_MTM_BLOCK": "univariate_only",
    "TIMEKAN_BLOCK": "univariate_only",
    "TIMES2D_BLOCK": "univariate_only",
    "VMD": "univariate_only",
    "WAVEFORM_BLOCK": "univariate_only",
    "WAVELET": "univariate_only",
    "WAVELETMIXER_BLOCK": "univariate_only",
    "XPATCH_BLOCK": "univariate_only"
  },
  "request": {
    "allow_optional_backends": false,
    "channels": 3,
    "length": 192,
    "prefer": "accuracy",
    "require_native": false,
    "top_k": 5
  }
}