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Title: Prediction bands for functional data based on depth measures Authors:  Antonio Elias - Universidad Carlos III de Madrid (Spain) [presenting]
Abstract: A new methodology is proposed for predicting a partially observed curve from a functional data sample. The novelty of our approach relies on the selection of sample curves which form tight bands that preserve the shape of the curve to predict, making this a deep datum. The involved subsampling problem is dealt by two algorithms specially designed to be used in conjunction with two different ways for computing central regions for visualizing functional data. From this merge we obtain prediction bands for the unobserved part of the curve in question. We test our algorithms by forecasting the Spanish electricity demand and imputing missing daily temperatures. The results are consistent with our simulation that show that we are able to predict at the far horizon.