A depth-based method for functional time series forecasting

TL;DR

This paper introduces a novel depth-based approach for forecasting functional time series, providing accurate predictions and reliable prediction bands, especially suited for periodically correlated data like electricity demand.

Contribution

The paper presents a new depth-based forecasting method that is computationally efficient and offers improved prediction accuracy over existing techniques for functional time series.

Findings

Accurate point forecasts for electricity demand

Narrow prediction bands with high coverage

Effective for periodically correlated processes

Abstract

An approach is presented for making predictions about functional time series. The method is applied to data coming from periodically correlated processes and electricity demand, obtaining accurate point forecasts and narrow prediction bands that cover high proportions of the forecasted functional datum, for a given confidence level. The method is computationally efficient and substantially different to other functional time series methods, offering a new insight for the analysis of these data structures.