{
  "model_type": "ClimateRNN",
  "architecture": "LSTM",
  "framework": "PyTorch",
  "version": "1.0.0",
  "hyperparameters": {
    "input_size": 4,
    "hidden_size": 96,
    "num_layers": 2,
    "dropout": 0.10027879545211107,
    "seq_length": 30,
    "learning_rate": 0.00037446563861783026,
    "batch_size": 32,
    "grad_clip_max_norm": 1.0
  },
  "features": {
    "input_features": [
      "meantemp",
      "humidity",
      "wind_speed",
      "meanpressure"
    ],
    "target_feature": "meantemp",
    "target_idx": 0,
    "feature_order": "Features must be provided in exact order: meantemp, humidity, wind_speed, meanpressure"
  },
  "preprocessing": {
    "scaler": "MinMaxScaler",
    "fit_on": "training_data",
    "scaler_file": "scaler.pkl"
  },
  "performance": {
    "test_mae": 1.93,
    "test_rmse": 2.422,
    "validation_mse": 0.007749,
    "unit": "celsius"
  },
  "training": {
    "dataset": "Daily Delhi Climate",
    "training_samples": 1170,
    "validation_samples": 292,
    "test_samples": 114,
    "training_date_range": "2013-01-01 00:00:00 to 2017-01-01 00:00:00",
    "test_date_range": "2017-01-01 00:00:00 to 2017-04-24 00:00:00",
    "optimizer": "Adam",
    "scheduler": "ReduceLROnPlateau",
    "epochs": 50
  },
  "inference": {
    "input_format": "30-day sequence of 4 climate features",
    "output_format": "Next-day temperature prediction in Celsius",
    "device": "cpu",
    "expected_latency_ms": 20
  },
  "metadata": {
    "created_at": "2026-05-15",
    "pytorch_version": "2.12.0+cu130",
    "python_version": "3.11+",
    "license": "MIT"
  }
}