Given a time series with job-submission counts, how can I predict which certain features about the jobs?

enter image description here

I need to predict how many jobs and which jobs arrived in some system. Using pandas.groupby, I've sliced the data into 15 minutes intervals.

I can predict how many jobs arrived but also need to predict which type of job will arrive and some other features.


I'm assuming the displayed time series shows number of jobs submitted per 15 minute interval.

Categorical features

Divide the time series per category. If the jobs can be divided into type1, type2, type3 then make a time series for each type and predict each series individually. So type1-time series has number of type1-jobs per 15 minute interval.

Continuous features

For continues features e.g time-to-do-job you can divide the jobs into categories of time00,time10, time20, time30 for jobs that take 0-9 minutes, 10-19 minutes, 20-29 minutes etc respectively. As before generate a time-series per division.

Depending on how much data you have and how it is distributed you can make more groups or space them differently.

| improve this answer | |
  • $\begingroup$ thanks it's seems like good option $\endgroup$ – Philip Konokhov Dec 27 '19 at 9:04

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.