Upload climate forecasting RNN model

README.md

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+---
+language: en
+license: mit
+tags:
+- climate
+- time-series
+- lstm
+- temperature-forecasting
+- pytorch
+datasets:
+- delhi-climate
+metrics:
+- mae
+- rmse
+---
+
+# Climate Forecasting RNN
+
+LSTM-based time series forecasting model for next-day temperature prediction.
+
+## Model Description
+
+This model predicts the next day's temperature based on 30 days of historical climate data (temperature, humidity, wind speed, and atmospheric pressure). It was trained on the Daily Delhi Climate dataset using hyperparameter optimization with Ray Tune.
+
+**Architecture**: LSTM (2 layers, 96 hidden units)
+**Framework**: PyTorch 2.12.0+cu130
+**Input**: 30-day sequence of 4 climate features
+**Output**: Next-day temperature in Celsius
+
+## Performance
+
+Evaluated on held-out test data (114 samples):
+
+- **MAE**: 1.930°C
+- **RMSE**: 2.422°C
+- **Validation MSE**: 0.007749
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+import pickle
+import json
+import numpy as np
+
+# Download model files
+model_path = hf_hub_download("DanielTobi0/climate-rnn-model", "pytorch_model.bin")
+scaler_path = hf_hub_download("DanielTobi0/climate-rnn-model", "scaler.pkl")
+config_path = hf_hub_download("DanielTobi0/climate-rnn-model", "config.json")
+
+# Load model architecture (you need the ClimateRNN class)
+with open(config_path, 'r') as f:
+    config = json.load(f)
+
+from src.model.architecture import ClimateRNN
+
+model = ClimateRNN(
+    input_size=config['hyperparameters']['input_size'],
+    hidden_size=config['hyperparameters']['hidden_size'],
+    num_layers=config['hyperparameters']['num_layers'],
+    dropout=config['hyperparameters']['dropout']
+)
+
+# Load weights
+model.load_state_dict(torch.load(model_path, map_location='cpu', weights_only=True))
+model.eval()
+
+# Load scaler
+with open(scaler_path, 'rb') as f:
+    scaler = pickle.load(f)
+
+# Prepare input (30-day sequence)
+sequence = [
+    [25.0, 60.0, 5.0, 1010.0],  # Day 1: [temp, humidity, wind_speed, pressure]
+    [26.0, 58.0, 6.0, 1012.0],  # Day 2
+    # ... 28 more days
+]
+sequence_scaled = scaler.transform(sequence)
+x = torch.tensor(sequence_scaled, dtype=torch.float32).unsqueeze(0)
+
+# Predict
+with torch.inference_mode():
+    prediction_scaled = model(x).item()
+
+# Inverse transform (temperature is at index 0)
+dummy = np.zeros((1, 4))
+dummy[0, 0] = prediction_scaled
+temperature = scaler.inverse_transform(dummy)[0, 0]
+print(f"Predicted temperature: {temperature:.2f}°C")
+```
+
+## Training Data
+
+**Dataset**: Daily Delhi Climate (2013-2017)
+**Training samples**: 1,170
+**Test samples**: 114
+**Features**: meantemp, humidity, wind_speed, meanpressure
+
+## Hyperparameters
+
+Optimized using Ray Tune with ASHA scheduler:
+
+- Hidden size: 96
+- Num layers: 2
+- Dropout: 0.1003
+- Sequence length: 30 days
+- Learning rate: 0.000374
+- Batch size: 32
+
+## Limitations
+
+- Trained only on Delhi climate data (may not generalize to other regions)
+- Requires exactly 30 consecutive days of input
+- Predicts only one day ahead
+- Does not account for extreme weather events or climate change trends
+
+## License
+
+MIT License

config.json

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+{
+  "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"
+  }
+}

pytorch_model.bin

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+size 459141

scaler.pkl

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