File size: 11,560 Bytes
ae97a0a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 | {
"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": "python",
"channel_names": [
"channel_a",
"channel_b",
"channel_c"
],
"method": "MSSA",
"params": {
"primary_period": 12,
"rank": 6,
"window": 24
},
"speed_mode": "exact"
},
"family": "SSA",
"implementation": "python",
"input_mode": "multivariate",
"maturity": "flagship",
"min_length": 24,
"multivariate_support": "shared-model",
"name": "MSSA",
"native_backed": false,
"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",
"shared_multivariate",
"accuracy_shortlist",
"multivariate_accuracy_bonus"
],
"score": 13.5,
"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": {
"CEEMDAN": "univariate_only",
"EMD": "univariate_only",
"GABOR_CLUSTER": "univariate_only",
"MA_BASELINE": "univariate_only",
"MEMD": "optional_backend_disabled",
"MSTL": "univariate_only",
"MVMD": "optional_backend_disabled",
"ROBUST_STL": "univariate_only",
"SSA": "univariate_only",
"STL": "univariate_only",
"VMD": "univariate_only",
"WAVELET": "univariate_only"
},
"request": {
"allow_optional_backends": false,
"channels": 3,
"length": 192,
"prefer": "accuracy",
"require_native": false,
"top_k": 5
}
}
|