Prediction of hierarchical time series using structured regularization and its application to artificial neural networks
This work addresses the need for coherent forecasts in hierarchical time series, which is important for applications like supply chain management, but it is incremental as it builds on existing two-phase methods.
The paper tackles the problem of predicting hierarchical time series while ensuring coherence between upper and lower levels, proposing a structured regularization method that simultaneously computes and reconciles forecasts, which improves prediction accuracy and computational efficiency as shown in experiments.
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.