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Title: Long-term prediction for high-dimensional regression Authors:  Sayar Karmakar - University of Florida (United States) [presenting]
Abstract: Time-aggregated prediction intervals are constructed for a univariate response time series in a high-dimensional regression regime. A simple quantile-based approach on the LASSO residuals seems to provide reasonably good prediction intervals. We allow for a very general possibly heavy-tailed, possibly long-memory and possibly non-linear dependent error process and discuss both the situations where the predictors are assumed to form a fixed or stochastic design. Finally, we construct prediction intervals for hourly electricity prices over horizons spanning 17 weeks and compare them to selected Bayesian and bootstrap interval forecasts