🤖 AI Summary
This work addresses the performance bottleneck in joint probabilistic forecasting of multivariate time series arising from the entangled modeling of marginal distributions and cross-series dependencies. To resolve this, the authors propose WIRED, a novel approach that decouples adaptive marginal prediction via expert aggregation from dependency structure reconstruction based on copulas. Specifically, marginal forecasts are generated using a CRPS-weighted adaptive ensemble, while dependencies are captured through Gaussian or Student-t copulas. The framework is rigorously evaluated via rolling-origin validation and ablation studies on both synthetic data and the EuStockMarkets benchmark. Results demonstrate the efficacy of the proposed architecture and reveal limitations in current CRPS-weighting strategies, showing that simple equal-weighting or bootstrap aggregation remains competitive for marginal modeling—offering new insights for designing regularized ensembles in probabilistic forecasting.
📝 Abstract
This paper presents WIRED, an R package algorithm for joint probabilistic forecasting of multiple related time series. WIRED combines a library of simple marginal predictive distributions, CRPS-based adaptive mixture weights, and a Gaussian or Student t copula for cross-series simulation. We evaluate the implementation in a benchmark with four synthetic data-generating processes (DGPs), three forecast horizons, 30 replicates per DGP-horizon pair, nine ablations and external baselines, and a rolling-origin study on the built-in EuStockMarkets data. The central contribution is architectural and diagnostic. WIRED separates adaptive marginal expert aggregation from dependence reconstruction; the benchmark supports explicit dependence modeling, but shows that the current CRPS-extrapolated softmax weighting is not yet robust enough to dominate simpler bootstrap or equal-weight alternatives. The paper therefore identifies a working layer of the design, a bottleneck in the marginal aggregation layer, and a concrete research path for more regularized probabilistic ensemble construction.