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README.md

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+---
+license: mit
+task_categories:
+  - tabular-classification
+tags:
+  - anomaly-detection
+  - time-series
+  - time-series-classification
+  - server-monitoring
+  - cybersecurity
+  - benchmark
+  - physics
+  - waveguard
+  - zero-training
+  - iot
+  - financial-data
+pretty_name: WaveGuard Anomaly Detection Benchmarks
+size_categories:
+  - 1K<n<10K
+---
+
+# WaveGuard Anomaly Detection Benchmarks
+
+Curated benchmark datasets for evaluating time-series and tabular anomaly detection models.
+Each dataset includes labeled training (normal) and test (mixed normal + anomalous) splits.
+
+## Datasets
+
+### 1. Server Metrics (`server_metrics/`)
+
+Simulated server health metrics with injected failure events.
+
+- **Features**: cpu, memory, disk_io, network, errors (5 numeric)
+- **Training**: 500 normal samples
+- **Test**: 100 samples (15 anomalous)
+- **Anomaly types**: CPU spike, memory leak, disk saturation, network flood
+
+### 2. Crypto Price Anomalies (`crypto_prices/`)
+
+Real cryptocurrency OHLCV data (BTC, ETH, SOL) from 2021-2026 with labeled flash crashes and pump events.
+
+- **Features**: open, high, low, close, volume (5 numeric per coin)
+- **Training**: 1200 normal daily candles per coin
+- **Test**: 600 candles per coin (labeled anomalies at known events)
+- **Source**: Yahoo Finance via yfinance
+
+### 3. Synthetic Time Series (`synthetic_timeseries/`)
+
+Controlled synthetic signals with known anomaly injection points.
+
+- **Patterns**: sinusoidal, trend, seasonal, random walk
+- **Anomaly types**: point (spike), contextual (subtle shift), collective (regime change)
+- **Training**: 200 clean windows per pattern
+- **Test**: 50 windows per pattern (10 anomalous each)
+
+## Format
+
+Each dataset is provided as Parquet files:
+
+```
+dataset_name/
+  train.parquet     # Normal samples only
+  test.parquet      # Mixed normal + anomalous
+  metadata.json     # Feature descriptions, anomaly counts, creation params
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("gpartin/waveguard-benchmarks", "server_metrics")
+train = ds["train"].to_pandas()
+test = ds["test"].to_pandas()
+```
+
+## Evaluation Protocol
+
+1. Train/fit your detector on `train.parquet` only
+2. Score each row in `test.parquet`
+3. Report: Precision, Recall, F1, AUC-ROC, Average Latency
+4. Compare against WaveGuard baseline in the model card
+
+## Citation
+
+```bibtex
+@dataset{waveguard_benchmarks2025,
+  title={WaveGuard Anomaly Detection Benchmarks},
+  author={Partin, Grant},
+  year={2025},
+  url={https://huggingface.co/datasets/gpartin/waveguard-benchmarks}
+}
+```

server_metrics/metadata.json

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+{
+  "name": "server_metrics",
+  "features": [
+    "cpu",
+    "memory",
+    "disk_io",
+    "network",
+    "errors"
+  ],
+  "n_train": 500,
+  "n_test": 100,
+  "n_anomalies": 15,
+  "anomaly_types": [
+    "cpu_spike",
+    "mem_leak",
+    "disk_sat",
+    "net_flood"
+  ],
+  "seed": 42
+}

server_metrics/test.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cd0f293bac4048cd34282474f347015d940db00e9f456effac229a767a31f98a
+size 7248

server_metrics/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c7842b92ca7184debf9304cdaa8e587ddae022ef618753d4acf343e54236347
+size 14463

synthetic_timeseries/metadata.json

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+{
+  "name": "synthetic_timeseries",
+  "patterns": [
+    "sinusoidal",
+    "trend",
+    "seasonal",
+    "random_walk"
+  ],
+  "window_size": 20,
+  "n_train": 800,
+  "n_test": 200,
+  "n_anomalies": 40,
+  "anomaly_types": [
+    "point_spike",
+    "context_shift",
+    "regime_change"
+  ],
+  "seed": 123
+}

synthetic_timeseries/test.parquet

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+oid sha256:d6116e269dd192543c5c5fdc4b09c56e247f692f67a54fc0e4898411b121b920
+size 48404

synthetic_timeseries/train.parquet

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+oid sha256:2fb7ebc07c7bb85b5e43e648145cf6adfee2a929b04ce34f18a8b0092a2776b5
+size 142131