Prediction of hierarchical time series using structured regularization and its application to artificial neural networks

TL;DR

This paper introduces a structured regularization approach for hierarchical time series prediction that ensures coherence across levels and improves accuracy and efficiency, especially when applied to neural networks.

Contribution

It proposes a novel regularization method that integrates hierarchical constraints directly into the prediction model, enabling simultaneous forecasting and reconciliation.

Findings

Outperforms existing methods in accuracy on synthetic and real datasets

Enhances computational efficiency in hierarchical time series prediction

Effective when integrated with neural network models

Abstract

This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of forecasts for corresponding lower-level time series. Previous methods for making coherent forecasts consist of two phases: first computing base (incoherent) forecasts and then reconciling those forecasts based on their inherent hierarchical structure. With the aim of improving time series predictions, we propose a structured regularization method for completing both phases simultaneously. The proposed method is based on a prediction model for bottom-level time series and uses a structured regularization term to incorporate upper-level forecasts into the prediction model. We also develop a backpropagation algorithm specialized for application of our method to artificial neural networks for time series prediction. Experimental results using synthetic and real-world datasets demonstrate the superiority of our method in terms of prediction accuracy and computational efficiency.