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Text: We use the lasso as a regression method with subset selection (for both the 'average' and the 'smoothing' type); the predictors are selected by including an ℓ 1 penalty on the vector of regression weights during the loss minimization, which effectively sets many of the weights to zero 54 . For comparison, we evaluate unweighted averages over N days similar to the eclipse day ('N-averages' from now on), as well as standard smoothing procedures (local regression, in both linear 5 and quadratic variants). Similarity between predictor and target days for the N-average is assessed as the pairwise Euclidean distance in the space spanned by times t • . Uncertainties for all methods can be quantified empirically by estimating the values for all t ∈ t • on all days d ∈ d • and subtracting the corresponding actual observations. This gives an error distribution for each time t ∈ t • . For the smoothing regression-the only method where the quantity of interest to us is actually the target-care needs to be taken to separate training and test data. To this end, we use four-fold cross-validation, so that approximately 1 year of **data** is in the test set at any time, and we calculate the error for a day only if it is in a test set. Figures 1 and 2 show the regression-based estimates and those obtained from comparison methods, respectively, for air temperature on the day of the eclipse at a few selected stations in and around La Serena. The box plots show the temperature depression T of the observation relative to the reference estimate. The plot elements representing uncertainty (shaded bands, boxes, whiskers and fliers) are constructed by adding the error distributions computed as described above to the point estimate. The regression methods' uncertainty is much reduced relative to that of the comparison methods; some but not all estimates of T fall outside of 95% of the calculated error distributions.<|eot_id|><|start_header_id|>assistant<|end_header_id|>

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