We plan to store our sensor time series data in cassandra
and use spark/spark-ts
to apply machine learning algorithms on it.
Unlike in the documentation, our time series data is irregular - unevenly spaced time series - as the sensors send the data event-based.
But most algorithms and models require regular time series.
Does
spark-ts
provide any function to transform the irregular time series to regular ones (using interpolation or time-weighted-average, etc.)?If not, what would be a recommended approach to solve that problem ?