Hierarchical Time Series Forecasting with Operator-Valued Kernels

Key points

• Hierarchical time series forecasting requires generating accurate predictions that are coherent across multiple levels of aggregation.1
• By designing structured operator-valued kernels, our approach enforces coherence directly within the hypothesis space, ensuring that all predictions satisfy hierarchical constraints by construction.1
• We further propose tailored kernel constructions for hierarchical data and derive excess risk bounds that characterize generalization performance.1
• Empirical results on synthetic and real-world datasets demonstrate the relevance of the proposed method.1

Sources (1)

• [1]Hierarchical Time Series Forecasting with Operator-Valued Kernels
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 11:32 AM

  We introduce a unified framework that formulates hierarchical forecasting as a multivariate regression problem in a vector-valued reproducing kernel Hilbert space.

  Hierarchical time series forecasting requires generating accurate predictions that are coherent across multiple levels of aggregation.

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