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Investigate Large Horizon Models #213
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Large Horizon => Long Term ??? |
…ies for larger horizons (H=12, 24, 36, 48)
Added movies of prediction intervals for Ozone and airline passengers models for increasing horizons (H=12, 24, 36, 48). https://github.com/antoinecarme/PyAF_Benchmarks/tree/master/model_visualizer |
…ns when performing model selection. WIP
Add a voting system. Condorcet method by default https://en.wikipedia.org/wiki/Condorcet_method Each model has performance measures for each horizon. The value of the performance measure is considered as a horizon vote (H voters). Longer horizons are weighted (the longer the model performs, the better it is). For the Condorcet method, each pair of models is compared (MAPE values). In a pair competition , the winning model (smaller MAPE) is assigned a score of 1, this score is reweighted by the horizon length. where By design, the higher the voting score, the better it is. |
H = 10 generated with this script : https://github.com/antoinecarme/pyaf/blob/issue_213_Large_Horizon_Models/tests/long_term_forecasts/test_yosemite_temps_Horizon_10.py |
TODO: Add some documentation about the new model selection procedure, with a detailed example. For the moment : |
TODO: Keep some kind of backward compatibility. Use a new training option to choose between Condorcet and the old method. Done. |
TODO : Do some "homework" about existing state of the art methods (FPP3 ?) !!! |
…rformance measures in debug mode.
…rformance measures in debug mode.
Advanced Review https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1475 |
Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701. https://www.sciencedirect.com/science/article/abs/pii/S0148296315001423 |
Measuring forecast accuracy |
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these perfs
… the first and last multistep forecast perfs. Updated these scripts
… the first and last multistep forecast perfs. Updated these scripts
Closing. |
Large Horizon Models (H large enough). Profiling for CPU/memory/speed.
Compute Prediction intervals for all tested models.
Use more sophistical forecast perf combination in model selection (mean ? max ?). decreasing time based weights ?
Take into account the shape of the prediction interval (esthetic for model precision).
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