SlowGuess/NeurIPS_2023_OCR huggingface dataset:
LSTM	Ridge	LGBM	Forest	FCα
HOPCPT
0.15	0.10	0.05	Δ Cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.000	0.006±0.000	Δ Cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.000	0.006±0,000	Δ Cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.000	0.005±0.000	Δ Cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.000	0,006±0.000	Δ Cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±0.00	Δ Cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ cov 0.004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0,004±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0.005±0.004	0.000±0.000	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0.005±0,77	0.004±0.00	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0.005±0.77	0.004±0.00	-0.018	-0.001	-0.012	-0.026	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0.005±0.77	0,628.2±0.9	-0.019	-0.001	-0.000	-0.022	-0.048
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0.005±0.77	688.0	-990.0	-990.0	-993.9	817.2
Δ Cov 0.005±0.005	0.006±0.00	0.005±	Δ Cov 0.005±0.77	681.2±0.9	-993.9	-993.9	-993.9	817.2
Winkler 0.17±0.01	0.24±0.01	0.24±0.01	Winkler 0.19±0.01	0.24±0.01	Winkler 0.19±0.01	Winkler 0.19±0.01	Winkler 0.19±0.01	Winkler 0.19±0.01	Winkler 0.19±0.01
Table 23: Performance of the evaluated CP algorithms and the CMAL baseline (see Appendix A.3) on the Streamflow dataset for the miscoverage levels  $\alpha \in \{0.05, 0.10, 0.15\}$ . Bold numbers correspond to the best result for the respective metric in the experiment (PI-Width and Winkler score), given that  $\Delta \mathrm{Cov} \geq -0.25\alpha$ , i.e. the specific algorithm reached at least approximate coverage (the result is grayed out otherwise). The error term represents the standard deviation over repeated runs (results without an error term are from deterministic models).
α	HopCPT	SPCI	EnbPI	NexCP	CopulaCPTS	CP/CF-RNN	CMAL
0.05	Δ Cov	-0.002±0.022	0.013±0.000	-0.042	-0.001	0.003	0.006	-0.004±0.004
PI-Width	1.91±0.20	2.57±0.00	2.53	3.23	3.44	3.63	2.4±0.08
Winkler	1.05±0.03	1.38±0.00	1.91	1.80	1.93	1.94	0.78±0.01
0.10	Δ Cov	0.001±0.041	0.027±0.000	-0.054	-0.000	0.005	0.009	-0.004±0.008
PI-Width	1.39±0.17	1.58±0.00	1.55	1.94	1.99	2.08	1.90±0.06
Winkler	0.79±0.03	0.91±0.00	1.27	1.21	1.28	1.29	0.63±0.01
0.15	Δ Cov	0.003±0.056	0.038±0.000	-0.061	0.001	0.005	0.009	-0.006±0.011
PI-Width	1.11±0.15	1.17±0.00	1.12	1.39	1.39	1.45	1.60±0.05
Winkler	0.66±0.03	0.71±0.00	0.98	0.95	0.99	1.00	0.55±0.03