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Pareto plots: accuracy vs fit + predict time

Times are summed over the 8 CV folds (fit + predict/score). They come from per-fold timings in the scores CSV when available, otherwise from the run log. Hardware and parallel load differ across runs (CPU vs GPU), so times are indicative rather than a controlled benchmark. The horizontal axis is log-scaled. MAPE uses the median prediction. Pinball loss is averaged over the 5%, 50% and 95% quantiles and all horizons; models without quantiles are omitted from the pinball figure. Lower is better on both axes. Table values are means across the 8 folds.

modelfit + predict (s)MAPE (%)pinball loss (MW)
hgb_mean_fast551.801-
hgb_mean2521.784-
tabicl_n8_full5231.704225.95
kumo_forecast6153.777-
kumo_tabular1,2731.405190.70
hgb_quantile_q72,1541.753244.21
chronos2_ctx40003,2941.706219.23
hgb_quantile_recal_q78,9211.844244.84
tabicl_forecast_ctx1024_n412,0512.141280.12

Paired differences vs baseline

MAPE is relative to hgb_mean; pinball loss is relative to hgb_quantile_q7. The baseline is shown at zero difference. Large markers: pooled mean difference over all test rows, with a 95% moving-block bootstrap interval (14-day blocks over daily differences; conditional on the fitted models, it ignores training randomness). Small dots: mean difference in each of the 8 CV folds (one dot per fold; hover shows the fold). Folds are not independent, so no t-interval across folds is shown. Dashed colored lines mark the Pareto frontiers.

Absolute accuracy vs fit + predict time

Means across the 8 CV folds, shown without error bars. Dashed colored lines mark the Pareto frontiers.