| # Real-Time Adaptive Signal Processing |
|
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| Evolve a real-time adaptive filtering algorithm for non-stationary time series data. The algorithm must filter noise while preserving signal dynamics and minimizing computational latency. |
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| ## Problem |
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| **Input**: Univariate time series with non-linear dynamics, non-stationary statistics, and rapidly changing spectral characteristics. |
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| **Constraints**: Causal processing (finite sliding window), fixed latency, real-time capability. |
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| **Multi-objective function**: |
| ``` |
| J(theta) = 0.3*S + 0.2*L_recent + 0.2*L_avg + 0.3*R |
| ``` |
| - **S**: Slope change penalty (directional reversals in filtered signal) |
| - **L_recent**: Instantaneous lag error |
| - **L_avg**: Average tracking error |
| - **R**: False reversal penalty (noise-induced trend changes) |
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| The evaluator tests on 5 synthetic signals: sinusoidal, multi-frequency, non-stationary, step changes, and random walk. |
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| ## Run |
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|
| ```bash |
| # From repo root |
| uv run skydiscover-run \ |
| benchmarks/math/signal_processing/initial_program.py \ |
| benchmarks/math/signal_processing/evaluator.py \ |
| -c benchmarks/math/signal_processing/config.yaml \ |
| -s [your_algorithm] \ |
| -i 100 |
| ``` |
|
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| ## Scoring |
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| - **combined_score**: Composite J(theta) metric (higher is better) |
| - Also reports: slope changes, correlation, lag error, noise reduction, processing time |
| |
| ## Files |
| |
| | File | Description | |
| |------|-------------| |
| | `initial_program.py` | Seed: basic moving average / weighted exponential filters | |
| | `evaluator.py` | Multi-objective evaluation across 5 synthetic test signals | |
| | `config.yaml` | LLM and evaluator settings | |
| | `requirements.txt` | Python dependencies | |
| |
