# Temporal-Compare Benchmark Results

## Test Configuration
- Dataset: Synthetic temporal data with Gaussian noise
- Window size: 32 time steps
- Task: Time-R1 style temporal prediction

## Results Summary

### Regression Task (MSE - Lower is Better)
| Backend  | Train Size | Epochs | MSE (Val) | MSE (Test) |
|----------|------------|--------|-----------|------------|
| Baseline | N/A        | N/A    | N/A       | 0.1120     |
| MLP      | 2000       | 15     | 0.1375    | 0.1281     |
| MLP      | 5000       | 20     | 0.1722    | 0.1424     |

### Classification Task (Accuracy - Higher is Better)
| Backend  | Train Size | Epochs | Accuracy  |
|----------|------------|--------|-----------|
| Baseline | N/A        | N/A    | 0.6467    |
| MLP      | 2000       | 15     | 0.3700    |
| MLP      | 1000       | 10     | 0.1667    |

## Key Observations

1. **Baseline Performance**: The naive baseline (predicting last value in window) performs surprisingly well:
   - MSE: ~0.11
   - Accuracy: ~65-70%

2. **MLP Challenges**: The simplified MLP without full backpropagation shows:
   - Regression: Competitive with baseline (MSE: 0.128 vs 0.112)
   - Classification: Underperforms baseline significantly (37% vs 65%)

3. **Training Dynamics**:
   - Lower learning rates (0.001) improve stability
   - More epochs don't always improve performance
   - The simplified SGD approach limits learning capacity

## Architecture Details

### MLP Implementation
- Architecture: Input(32) → Hidden(64) → Output(1 or 3)
- Activation: ReLU
- Training: Simplified SGD with numerical gradient approximation
- Weight Init: Xavier/He initialization

### Baseline
- Strategy: Returns last value in temporal window
- Classification: Maps continuous values to 3 classes via thresholds

## Compilation Features
✅ Successfully builds with all backends:
- `baseline`: Always available
- `mlp`: Native Rust implementation
- `ruv-fann`: Feature-gated, compiles successfully

## Future Improvements
1. Implement full backpropagation for better gradient flow
2. Add momentum and adaptive learning rates
3. Implement proper cross-entropy loss for classification
4. Add validation-based early stopping
5. Integrate actual ruv-fann backend implementation