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README.md

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----
-license: mit
----
+---
+library_name: tensorflow
+tags:
+- time-series-forecasting
+- terrorism-forecasting
+- lstm
+- bidirectional
+- global-terrorism-database
+- security-analytics
+datasets:
+- gtd
+metrics:
+- rmse
+- mae
+- r2
+license: mit
+language:
+- en
+---
+
+# BiLSTM for Terrorism Event Forecasting
+
+Bidirectional LSTM model for weekly terrorism event forecasting using 46 years of Global Terrorism Database (GTD) data.
+
+**Paper**: [Predicting the Unpredictable: Bidirectional LSTM Networks for Terrorism Event Forecasting](https://arxiv.org/abs/2510.15136)
+
+## Model Performance
+
+| Model | RMSE ↓ | MAE | R² | Improvement |
+|-------|--------|-----|-----|-------------|
+| **BiLSTM (this model)** | **6.38** | **3.82** | **0.556** | **Baseline** |
+| LSTM+Attention | 9.19 | 5.37 | 0.264 | -30.6% |
+| Linear Regression | 9.89 | 5.85 | 0.176 | -35.5% |
+| SARIMA | 11.52 | 6.78 | -0.090 | -44.6% |
+
+**Key Achievement**: 37% improvement over best classical baseline (Linear Regression)
+
+## Model Architecture
+
+- **Type**: Bidirectional LSTM (2 layers)
+- **Input Shape**: (30 weeks, 13 features)
+- **Output**: Single value (next week's attack count)
+- **Parameters**: ~36,673
+- **Framework**: TensorFlow 2.13.0
+
+### Architecture Details:
+
+        Input(30, 13) 
+             ↓ 
+Bidirectional LSTM(64) + Dropout(0.2) 
+             ↓ 
+Bidirectional LSTM(32) + Dropout(0.2) 
+             ↓ 
+   Dense(32, ReLU) + Dropout(0.2) 
+             ↓ 
+      Dense(1, Linear)
+
+
+## Training Data
+
+- **Dataset**: Global Terrorism Database (START consortium)
+- **Time Period**: 1970-2016 (46 years)
+- **Resolution**: Weekly aggregation (2,400 weeks)
+- **Train/Val/Test**: 70%/15%/15% (chronological split)
+
+### Features (13 total):
+
+1. **Lag Features (1)**: 52-week lag
+2. **Rolling Statistics (4)**: 4-week & 12-week mean/std
+3. **Temporal Encoding (4)**: Year, week, month, quarter
+4. **Casualty Features (3)**: Killed, wounded, total
+5. **Geographic (1)**: Region encoding
+
+## Usage
+
+```python
+import tensorflow as tf
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="Davidavid4/bilstm-terrorism-forecasting-gtd",
+    filename="bidirectional_lstm_best.h5"
+)
+
+# Load model
+model = tf.keras.models.load_model(model_path)
+
+# Prepare input: shape (batch_size, 30, 13)
+# - 30 weeks of historical data
+# - 13 features per week
+
+# Make predictions
+predictions = model.predict(X_test)