Creative Fatigue Detection Eval

---
name: creative-fatigue-detection-eval
description: Evaluates change point detection algorithms for identifying the onset of creative fatigue in digital advertising campaigns. It probes early warning capabilities, precision-recall trade-offs, and detection latency against ground-truth performance degradation events. Use when the user wants to benchmark on Synthetic Gradual Decline, Synthetic Sharp Decline, or asks about evaluating this task. Reports Delay (days).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2509.09758
  bibtex_key: [REDACTED]
  confidence: medium
---

# creative-fatigue-detection-eval

> A Path Signature Framework for Detecting Creative Fatigue in Digital Advertising — Shaw (2025) (arXiv:2509.09758, 2025)

## What this evaluates

Evaluates change point detection algorithms for identifying the onset of creative fatigue in digital advertising campaigns. It probes early warning capabilities, precision-recall trade-offs, and detection latency against ground-truth performance degradation events.

## Datasets

- **Synthetic Gradual Decline** — total ?; splits: test (-1)
- **Synthetic Sharp Decline** — total ?; splits: test (-1)

## Metrics

- `Precision` — range: [0, 1]
  - Ratio of correctly detected fatigue change points to all detected change points.
- `Recall` — range: [0, 1]
  - Ratio of correctly detected fatigue change points to all actual fatigue change points.
- `F1-Score` — range: [0, 1]
  - Harmonic mean of Precision and Recall.
- `Delay (days)` **(primary)** — range: other
  - Detected change date minus ground-truth change date. Negative values indicate early warning.

## Input / output format

**Input**: Daily performance time-series data for digital advertising creatives, transformed into geometric paths via path signature analysis.

**Output**: Detected change point dates indicating the onset of creative fatigue.

## Scoring recipe

```python
def compute_metrics(detected_dates, ground_truth_dates):
    # Match detected to ground truth within a tolerance window
    tp = sum(1 for d in detected_dates if any(abs(d - g) <= tolerance for g in ground_truth_dates))
    precision = tp / len(detected_dates) if detected_dates else 0
    recall = tp / len(ground_truth_dates) if ground_truth_dates else 0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
    delays = [d - g for d, g in zip(detected_dates, ground_truth_dates)]
    avg_delay = sum(delays) / len(delays) if delays else 0
    return precision, recall, f1, avg_delay
```

## Common pitfalls

- Low precision (0.14) is intentional to maximize recall and enable early warnings; misinterpreting it as poor performance ignores the early-warning design goal.
- Negative delay values indicate early detection, not system lag; readers may incorrectly treat them as errors.
- Traditional baselines (MA Crossover, Rolling Regression) fail catastrophically on sharp declines due to violated statistical assumptions, making direct comparison without context misleading.

## Evidence (verbatim from paper)

> The performance metrics for each method on the two synthetic datasets—one with a gradual decline and one with a sharp decline—are summarised in Table 1. ... For clarity, we define detection delay as the detected change date minus the ground-truth change date; negative values therefore indicate an early warning. The method demonstrates a high recall of 1.00, ensuring it never misses actual fatigue events—critical when each missed detection represents continued wastage. The precision of 0.14 reflects our method’s sensitivity to a broad spectrum of performance changes, not just monotonic decline.

## Citation

```bibtex
@misc{shaw2025pathsignature,
  title={A Path Signature Framework for Detecting Creative Fatigue in Digital Advertising},
  author={Shaw (2025)},
  year={2025},
  note={arXiv:2509.09758}
}
```

- arXiv: 2509.09758