Time Series data: How to convert it in a streaming data? 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.
It depends on the answer to these questions:


How is the real-time data provided?
How often and in which format are the predictions required?


It's common to use a database in such a setting. So the ML prediction process would be called regularly, query this database to get the latest instances and generate predictions in the required format (possibly storing the predictions in the database as well).