Dimensionality reduction in forecasting with temporal hierarchies

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Abstract

Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a priori which part of the error structure is most important. To address these issues, we propose to use eigendecomposition for dimensionality reduction when reconciling forecasts to extract as much information as possible from the error structure given the data available. We evaluate the proposed estimator in a simulation study and demonstrate its usefulness through applications to short-term electricity load and financial volatility forecasting. We find that accuracy can be improved uniformly across all aggregation levels, as the estimator achieves state-of-the-art accuracy while being applicable to hierarchies of all sizes.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Technical University of Denmark
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Sannolikhetsteori och statistik

Nyckelord

Originalspråkengelska
Sidor (från-till)1127-1146
Antal sidor20
TidskriftInternational Journal of Forecasting
Volym37
Utgåva nummer3
Tidigt onlinedatum2021 jan 10
StatusPublished - 2021
PublikationskategoriForskning
Peer review utfördJa