Anomaly detection on multidimensional time series

Question

I have relatively little knowledge of unsupervised machine learning.

I'm working on a project that aims to find anomalies in a set of n data, measured every x ms, and we have no label on all these data. So I'm here to ask you which algorithms/approach should be the best?

Edit : I'm working in an industrial context, and my data are sensor readings (temperature, pressure, power consumption, etc.) all from the same equipment.

Answer 1

One option is to treat it as a "supervised learning" problem.

Since

a set of n data, measured every x ms

You have a time series. This can be treated as a supervised problem.

Say that you have observations for 10 minutes. In supervised learning, each sample would be :

• X : Set of last t-1 , t-2, t-3 and t-N observations (N is fixed, say 300)
• Y : Observation t

You can train a model to predict next observation, given last 300 observations. If actual value is beyond a certain % from the prediction, it is an anomaly.

Example : https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/

Answer 2

If you DO NOT have hundreds of features, then consider k-Nearest Neighbour (kNN) based outlier detection.

For a window of w unit times

1. calculate the median distances for the features (power,temp,..etc)
2. For each time stamp, calculate the kNN distance

Then you can select top 1% data points with highest deviations in kNN distances compared to the median kNN distance.