Coherency Loss for Hierarchical Time Series Forecasting

In hierarchical time series forecasting, some series are aggregated from others, producing a known coherency metric between series. We present a new method for enforcing coherency on hierarchical time series forecasts. We propose a new loss function, called Network Coherency Loss, that minimizes the coherency loss of the weight and bias of the final linear layer of a neural network. We compare it against a baseline without coherency and a state of the art method that uses projection to strictly enforce coherency. We find that, by choosing our Network Coherency Loss parameters based on validation data, for four datasets of varying sizes we produce improved accuracy over our two benchmark models. We also find that, when compared to an alternative loss function also designed to produce coherency, our Network Coherency Loss function produces similar accuracies but improves the coherency on the test data.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Hensgen, Michael Lowell

Advisor dc:contributor.advisor

• Perakis, Georgia

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156799

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156799