config.json

{
  "model_type": "fela-ts",
  "architectures": [
    "FelaTsModel"
  ],
  "auto_map": {
    "AutoConfig": "configuration_ts.FelaTsConfig",
    "AutoModel": "modeling_ts.FelaTsModel"
  },
  "library_name": "pytorch",
  "arch": "FELA_TS",
  "note": "FELA time series forecaster, about 1.76M params. RevIN, patch embedding, FNO spectral mixer blocks, linear head. Trained on electricity, 321 channels, L=512, H=96.",
  "C": 321,
  "L": 512,
  "H": 96,
  "patch": 16,
  "stride": 8,
  "D": 128,
  "modes": 16,
  "nblk": 3,
  "input_shape": [
    1,
    512,
    321
  ],
  "input_desc": "history window shape (1, 512, 321): 512 past steps of 321 channels, standardized per channel on training statistics. RevIN normalizes instances inside the model.",
  "complex_keys": [
    "blocks.0.fno.w",
    "blocks.1.fno.w",
    "blocks.2.fno.w"
  ]
}