I have some samples that move on a rail for a few minutes. During this time, some forces act on these samples. For example, I have M samples, for each sample, I have N features that are measured L times for each sample. The dimension of my data is(M*L, N). I want to do anomaly detection to predict the anomaly before it occurs. I have no idea how to approach this problem. Does anyone have an idea or can give me a reference work to read?
I have a data set where the samples are marked as anomalous and ok.
The data looks like this:

I have some samples that move on a rail for a few minutes. During this time, some forces act on these samples. For example, I have M samples, for each sample, I have N features that are measured L times for each sample. The dimension of my data is(M*L, N). I want to do anomaly detection to predict the anomaly before it occurs. I have no idea how to approach this problem. Does anyone have an idea or can give me a reference to read?
I have a data set where the samples are marked as anomalous and ok.
The data looks like this:

I have some samples that move on a rail for a few minutes. During this time, some forces act on these samples. For example, I have M samples, for each sample, I have N features that are measured L times for each sample. The dimension of my data is(M*L, N). I want to do anomaly detection to predict the anomaly before it occurs. I have a data set where the samples are marked as anomalous and ok.
The data looks like this:

I have some samples that move on a rail for a few minutes. During this time, some forces act on these samples. For example, I have M samples, for each sample, I have N features that are measured L times for each sample. The dimension of my data is(M*L, N). I want to do anomaly detection to predict the anomaly before it occurs. I have no idea how to approach this problem. Does anyone have an idea or can give me a reference work to read?
I have a data set where the samples are marked as anomalous and ok.
The data looks like this: