AnomalyBERT

Encoder-only transformer for anomaly detection in univariate timeseries data. Exported as ONNX for lightweight inference without PyTorch.

Model Description

• Architecture: Encoder-only transformer (BERT-style) with custom multi-head self-attention, sinusoidal positional encoding, and an MLP anomaly scoring head
• Input: Univariate timeseries as (timestamp, value) pairs
• Output: Top-N anomalous points ranked by anomaly score in [0, 1]
• Training: Supervised with BCE loss on labeled synthetic data (8 anomaly scenarios)
• Framework: PyTorch (training), ONNX Runtime (inference)
• CPU only: No GPU required

Architecture

Normalized values → Sliding Window (size=16, stride=1)
  → ValueEmbedding: Linear(1 → 64)
  → Sinusoidal Positional Encoding
  → Transformer Encoder (2 layers, 4 heads, d_ff=128)
  → AnomalyScoringHead: MLP → Sigmoid
  → Overlapping Window Aggregation → Top-N

How to Use

With ONNX Runtime (Python)

import numpy as np
import json
import onnxruntime as ort

# Load model and metadata
session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
meta = json.load(open("model.onnx.json"))

# Normalize input (using saved min/max from metadata)
norm = meta["normalizer"]
values = np.array([...], dtype=np.float32)
normalized = (values - norm["min"]) / (norm["max"] - norm["min"])

# Run inference on a window
window = normalized[:16].reshape(1, -1).astype(np.float32)
scores = session.run(None, {"input": window})[0]

With AnomalyBERT Library

pip install -e ".[onnx]"
anomalybert detect --model model.onnx --input data.csv --top-n 10

from anomalybert.inference.onnx_detector import OnnxAnomalyDetector

detector = OnnxAnomalyDetector("model.onnx")
results = detector.detect(timestamps, values, top_n=10)

Files

File	Description
model.onnx	ONNX model graph and weights
model.onnx.json	Model config and normalizer parameters

Training Data

Trained on 8 synthetic anomaly scenarios: point anomaly, contextual anomaly, collective anomaly, seasonal with anomaly, trend shift, noise burst, frequency change, and flat signal. Each scenario generates randomized timeseries with labeled anomaly positions and probabilities.

Limitations

• Trained on synthetic data only; may require finetuning for real-world domains
• Univariate timeseries only (single value column)
• Designed for small-to-medium series (10s to 100s of samples)

Source

GitHub: anomalyBert