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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. 24-hr forecast window
   2. 0-hr latency
   3. use only 00:00 UT time stamps (to avoid SDO 24-hr periodic orbital effects)
   4. create individual training configuration files separately using Blos and Br properties for flaring levels of:
      1. C-class only (i.e., >= C1.0 and < M1.0)
         algorithm_cclass_24_0_00_Blos_all 
         algorithm_cclass_24_0_00_Br_all
      2. M-class only (i.e., >= M1.0 and < X1.0)
         algorithm_mclass_24_0_00_Blos_all
         algorithm_mclass_24_0_00_Br_all
      3. X-class only (i.e., >= X1.0)
         algorithm_xclass_24_0_00_Blos_all
         algorithm_xclass_24_0_00_Br_all
      4. Above M-class (i.e., >= M1.0)
         algorithm_abovem_24_0_00_Blos_all
         algorithm_abovem_24_0_00_Br_all
      5. Above C-class (i.e., >= C1.0) 
         algorithm_abovec_24_0_00_Blos_all
         algorithm_abovec_24_0_00_Br_all
   5. There is no explicit reason why we should use all properties (but is worth doing for completeness), so another set of 10x configuration files should be prepared for a reduced set of "optimized"/"feature selected" properties
      1. C-class only (i.e., >= C1.0 and < M1.0)
         algorithm_cclass_24_0_00_Blos_opt
         algorithm_cclass_24_0_00_Br_opt
      2. M-class only (i.e., >= M1.0 and < X1.0)
         algorithm_mclass_24_0_00_Blos_opt
         algorithm_mclass_24_0_00_Br_opt
      3. X-class only (i.e., >= X1.0)
         algorithm_xclass_24_0_00_Blos_opt
         algorithm_xclass_24_0_00_Br_opt
      4. Above M-class (i.e., >= M1.0)
         algorithm_abovem_24_0_00_Blos_opt
         algorithm_abovem_24_0_00_Br_opt
      5. Above C-class (i.e., >= C1.0)
         algorithm_abovec_24_0_00_Blos_opt
         algorithm_abovec_abovec_24_0_00_Br_opt
   6. NOTE: there may be no point doing X-class only for either of these all or "optimized"/"feature selected" property set cases, given their rarity in the training time period
   7. NOTE: can run configurations with a combination of Blos and Br properties (possible filename tag Bmix) that would need to be run for its own 5 all and "optimized"/"feature selected" property sets both with their own flaring level scenarios – therefore creating another 10 configuration files (or 4 8 if "X-class only" case cases left out)
      
4. Run all 20 training configuration parameter files (or 16 if "X-class only" cases left out)
5. Write variables of all 20 trained prediction models into Prediction Configuration DB (or 16 if "X-class only" cases left out)
6. Integrate next prediction algorithm and repeat steps 2–5
7. Prediction DB can be filled for each integrated prediction algorithm by launching all 20x (or 16x) 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

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