How do I choose the right anomaly detection algorithm?

How do I choose the right anomaly detection algorithm?

Choosing the right anomaly detection algorithm depends on your data type, the nature of anomalies, and your goals. What kind of data are you working with?

I have time series data with seasonal patterns.

For time series data, you might consider algorithms like Seasonal Decomposition of Time Series (STL) or ARIMA. Are you looking for a supervised or unsupervised approach?

I'm looking for an unsupervised approach.

In that case, you could explore Isolation Forest or DBSCAN. Both are effective for detecting anomalies in time series data without labeled examples.

What factors should I consider when evaluating these algorithms?

Consider factors like the algorithm's sensitivity to noise, computational efficiency, and how well it handles different types of anomalies. Would you like more details on any specific algorithm?