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chronos-microgru

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probabilistic-forecasting
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chronos-microgru
File size: 1,987 Bytes
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{
  "model": "Chronos MicroGRU",
  "parameters": 8208,
  "context": 32,
  "horizon": 8,
  "channels": [
    "temperature",
    "pressure",
    "vibration",
    "current",
    "flow",
    "packet_rate"
  ],
  "train_windows": 3830,
  "validation_windows": 948,
  "test_windows": 961,
  "best_epoch": 38,
  "variance_scale": 1.05,
  "model_test": {
    "normalized_rmse": 0.26626020669937134,
    "normalized_mae": 0.19292967021465302,
    "gaussian_nll": -1.0139933824539185,
    "interval_90_coverage": 0.8862946583420048,
    "mean_interval_width": 0.7816126942634583
  },
  "persistence_test": {
    "normalized_rmse": 0.3729550838470459,
    "normalized_mae": 0.26573994755744934
  },
  "rmse_improvement_percent": 28.60796963728523,
  "model_channel_metrics": {
    "temperature": {
      "rmse": 1.0075331926345825,
      "mae": 0.8248881101608276
    },
    "pressure": {
      "rmse": 0.8195824027061462,
      "mae": 0.6552034616470337
    },
    "vibration": {
      "rmse": 0.0874105840921402,
      "mae": 0.06943069398403168
    },
    "current": {
      "rmse": 0.3007839322090149,
      "mae": 0.23933608829975128
    },
    "flow": {
      "rmse": 0.8989439606666565,
      "mae": 0.7173601984977722
    },
    "packet_rate": {
      "rmse": 4.041879177093506,
      "mae": 3.2288103103637695
    }
  },
  "persistence_channel_metrics": {
    "temperature": {
      "rmse": 1.1200745105743408,
      "mae": 0.8925882577896118
    },
    "pressure": {
      "rmse": 1.1493462324142456,
      "mae": 0.9189407229423523
    },
    "vibration": {
      "rmse": 0.1277424842119217,
      "mae": 0.10088279843330383
    },
    "current": {
      "rmse": 0.43681859970092773,
      "mae": 0.3465101718902588
    },
    "flow": {
      "rmse": 1.259148359298706,
      "mae": 1.0023177862167358
    },
    "packet_rate": {
      "rmse": 5.790362358093262,
      "mae": 4.628175735473633
    }
  }
}