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1. Integrate prediction algorithm X into the infrastructure (personally I don’t mind which, but Michele’s suggestion of 1 supervised and 1 unsupervised is good)
2. The training time range to be used will be from 00:00:00 UT on 14-Sep-2012 (start of SHARP NRT data availability) to 23:59:59 UT on 31-Dec-2015
   1. Concern was raised previously about number of features for training, so a large training time range seems to be preferred
   2. This allows for the testing time range to be from 00:00:00 UT on 1-Jan-2016 to <most recently processed time stamp>, but this will be performed under WP5
3. Run integrated prediction algorithm with the following training configuration settings:
   1. Use only 00:00 UT time stamps (to avoid SDO 24-hr periodic orbital effects)
   2. 24-hr forecast window
   3. 0-hr latency
   4. create separate training configuration files for flaring levels of:
      1. C-class only (i.e., >= C1.0 and < M1.0)
      2. M-class only (i.e., >= M1.0 and < X1.0)
      3. X-class only (i.e., >= X1.0)
      4. Above M-class (i.e., >= M1.0)
      5. Above C-class (i.e., >= C1.0)
4. Run all 5 training configuration parameter files
5. Write variables of all 5 trained prediction models into Prediction Configuration DB
   1. Personally, I’m not sure if separate entries or a grouped JSON entry is better
6. Integrate next prediction algorithm and repeat steps 2–5
7. Prediction DB can be filled for each integrated prediction algorithm by launching all 5x trained prediction models for that algorithm on the chosen testing time range
   1. NOTE: the SDO/HMI image alignment bug from 13-Apr-2016 onwards will limit the availability of properties to make predictions from, until the replacement HMI data are available (UPSud is monitoring and downloading when available)
8. Broader WP5 validation can be explored by choosing different durations of forecast window and repeating steps 2–5 and 7 for all integrated prediction algorithms

File name for trained

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model:

In order to save the trained model with a name decided by the user, it is sufficient to write it in the configuration json file in the field "description":

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"algorithm": {
                   "phase": "execution",                           <--- do not touch
                   "config_name": "HybridLasso_test",  <---- HERE
                   "description": "HybridLasso"
}

The writing of the trained model is working locally, let see if other fixings are needed once we run them on the cluster.

Example Configuration JSON

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