Table 4.

Results of the addition study in the three regions: Algorithm 1 with Poisson for f and identity for g, $t_{\mathrm{s}}=0$, $t_{\mathrm{i}}=5$, $t_{\mathrm{c}}=84$, $t_{\mathrm{e}}=108$ and $h=1$. We denote by w: situation in the test period with the added variable and wo: situation in the test period without the indicated variable. Ratios of w/wo are reported. Note that, for the metrics such as RMSE, MASE and MARE, a ratio lower than 1 implies an improvement of the model when the variable is added as an explanatory variable. A ratio of $R_{\mathrm{COR}}^2$ higher than 1 indicates that adding the variable improves the forecasts.

Regions	Variable	RMSE w/wo	MASE w/wo	MARE w/wo	${\mathit{R}}_{\mathbf{COR}}^2$ w/wo
Dakar	Rainfall	1.18	1.02	0.99	1.43
	Temperature	0.99	1.05	1.15	1.04
	Humidity	0.94	1.04	1.06	1.11
Fatick	Rainfall	1.24	1.22	1.04	1.34
	Temperature	1	1.06	1.08	1.1
	Humidity	1.18	1.05	0.77	1.34
Kedougou	Rainfall	0.99	0.97	0.92	1.02
	Temperature	0.96	0.95	0.88	1.05
	Humidity	0.98	0.93	0.7	1.03