![]() For a given time series example that you want to predict, find the most similar time series in the training set and use its corresponding output as the prediction. Then use well-known classification algorithms (Naive Bayes, SVMs, etc.) with these features to make a prediction. Extract features from the time series like its mean, maximum, minimum, and other differential features. ![]() This was not a very straight-forward problem to tackle because it seemed like there two possible strategies to employ. churn or not churn) with a time series as a predictor. So this is a binary-valued classification problem (i.e. I recently ran into a problem at work where I had to predict whether an account would churn in the near future given the account’s time series usage in a certain time interval.
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