I have a dataset that only has two columns, ID and DATA, and 100 rows. Below is the example of how my dataset looks like:
ID DATA --- ---------------- 1 ac 09 bb 46 2 4f cd e2 3 ae bc 1 ac 09 bc 46 2 4f ce e2 3 ae bd
I would like to setup up algorithms for detecting an anomaly in time series, and I plan to use time series while at the same time data classification for that:
I've wondered whether:
I can develop Time Series model on 'ID' column to measure frequencies between the IDs in the flow, and at the same time I build a classification model on 'ID' and 'DATA' column, where 'DATA' will be classified based on categorical label 'ID'?
Are there any techniques that could parallelize training set data? I could train a time series model first, and then train a classifier to classify them based on their classes/labels, but I'm just thinking that if there's an algorithm that could train both in parallel.
I assumed that my case is multivariate time series. I was wondering how to handle spaces in the 'DATA' column?
Can I just sum up the errors in prediction from both models to indicate anomaly? How can I use these errors to build an anomaly detection model?