Hi @sebhrusen, @PGijsbers,
In this PR there are the following changes, I would highly appreciate it if you could review them.
Renaming forecast_range_in_steps to forecast_horizon_in_steps, because it is much more popular in the research field.
Fixed metrics calculation. A) Renamed y_past_period_error to naive_1_error, because we do not know periodicity/seasonality. B) Renamed ncrps (Normalized Continuous Ranked Probability Score) to mwql (Mean Weighted Quantile Loss), because that is what is actually calculated. This is an approximation of ncrps. C) Added item_id column to results, to be able to calculate itemwise_mean. D) Introduced finite_mean to calculate correct metrics, even if there are some NaNs.
Hi @sebhrusen, @PGijsbers, In this PR there are the following changes, I would highly appreciate it if you could review them.
Renaming
forecast_range_in_stepstoforecast_horizon_in_steps, because it is much more popular in the research field.Fixed metrics calculation. A) Renamed
y_past_period_errortonaive_1_error, because we do not know periodicity/seasonality. B) Renamedncrps(Normalized Continuous Ranked Probability Score) tomwql(Mean Weighted Quantile Loss), because that is what is actually calculated. This is an approximation ofncrps. C) Addeditem_idcolumn to results, to be able to calculateitemwise_mean. D) Introducedfinite_meanto calculate correct metrics, even if there are some NaNs.Added GluonTS framework, because it allows to calculate multiple timeseries baselines. GluonTS_* 1) Prophet, 2) DeepAR, 3) NBEATS, 4) NPTS, 5) SeasonalNaive, 6) SimpleFeedForward, 7) MQCNN, 8) MQRNN, 9) TFT, 10) ARIMA, 11) ETS, 12) STL-AR, 13) Theta.
Added improvements for AutoGluonTS. A) Do not
deepcopythe dataset to save memory. B) Use correct module to retrieve the AutoGluon version.FYI: Deviation starts with commit f7c9501dc5eea2d5808dbd6ec7e915e84c0f4858. FYI: @Innixma @canerturkmen @gidler