CODEWITHSUNDEEP
pythonrmachine-learningscikit-learn
Darshan Chaudhary
Darshan Chaudhary

Reputation: 2233

Implementing online learning with time series

I have a classification problem with time series data.

Each example has 10 variables which are measured at irregular intervals and in the end the object is classified into 1 of the 2 possible classes (binary classification).

I have only the final class of the example to learn from during training. But when given a new example, I would like to make a prediction at each timestamp (in an online manner). So, if the new example had 25 measurements, I would like to make 25 predictions of its class; one at each timestamp.

The way I am implementing this currently is by using the min, mean and max of the measurements of its 10 variables till that point as features for classification. Is this optimal ? What would be a better way.

Upvotes: 1

Views: 415

Answers (1)

Gaurav
Gaurav

Reputation: 1587

If you have to make predictions at each time stamp, then this doesn't become a a time series problem (unless you plan to use the sequence of previous observations to make your next prediction, in which case you will need to train a sequence based model). Assuming you can only train a model based on the final data you observe, there can be many approaches, but I'd recommend you use Random Forest with large number of trees and 3 or 4 variables in each tree. That way even if some variables don't give you the desired input other trees can still make predictions to a fair amount of accuracy. Besides this there can be many ensemble approaches.

The way you're currently doing may be a very loose approximation and practical but doesn't make much statistical sense.

Upvotes: 1

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