I have a time series data which is available in offline csv format. I am using this data to create anomaly detection model. Although I could create this model to predict anomalies in this dataset, I need to use this model when data is real time. Every second data point will be coming and then if point is anomalous I need model to raise an anomaly.
Any ideas how should I implement this?
Edit - Data is updated regularly in computer, file is updated as data is streaming in this file only. Predictions are required for the realtime anomaly detection as decision will have to be made to stop the process when such prediction for detection of anomaly is made.