Time Series Anomaly Detection from Data vs Image

Question

I was assigned with project to do anomaly detection on for our company KPI. I googled and found AnomalyDetection by Twitter. There was an idea from my colleague to do the anomaly detection on the graph images (comparing with previous week images to identify anomaly points) instead of using time-series raw data.

I am not familiar with the Anomaly Detection, anyone here experienced and able to advice which one is better (Anomaly Detection from data or image) in term of:

1. Accuracy 2. Storage 3. Processing

Answer 1

Advantages:

• Data-agnostic. Can theoretically be ran on anything where one can get an image/visualization out.
• Image models are relatively well understood.
• Pretrained models are available.

Disadvantages:

• Requires much more data to learn useful model. The image pixel space is much more complicated than the time-series it represents. Probably at least 100x.
• Requires much more compute power. Both at training time, and at prediction time. Probably at least 100x.
• Requires much more storage for datasets. Probably at least 100x.
• Sensitive to changes in visualization. A change in tickmarks or font for example would be an anomaly. Even a change in image compression may impact, if not controlled for.
• Lose explain-ability. May be hard to know why a certain image is anomaly, even for simple cases like a mean shift.
• Much more complex model setup and infrastructure needed

For an application like Anomaly Detection on Time Series on metrics, I would not recommend doing it. I am not even sure I have seen it studied. I think it is unlikely that a high performing Anomaly Detection system for metrics can be built effectively with image processing on graphs. Anomalies are typically quite rare, which means that it is a "low data" scenario. But also many anomalies are quite simple, and can be detected with simple methods - as basic as well chosen thresholds can go a long way. Using image processing does not help with any of these challenges, in fact it is worse in most regards.