In terms of the **data** matrix X with rows corresponding to days and columns to times, the 'average' type regression estimate for d • is given by where β is an estimate for the (column) vector of regression coefficients β and Ŷ is the regression estimate. This expression corresponds to an unweighted average over N (selected) days if β contains N elements with value 1/N and otherwise zeros. Viewed as a regression, an intercept is included by adding a column of ones to X T , and the estimate β d • t • for β is obtained by minimising a loss function. We denote with the subscripts the fact that β is estimated by using the observation at d • but only the times t • (excluding the eclipse) as a target. The 'smoothing' approach consists in using the columns of X corresponding to t • as the regression's targets and all remaining columns X dt • as predictors: