Halo: Improving forecast accuracy through heteroscedastic estimation
Updated 34 min ago · first seen 11 Sept 2026
paper_01M294FNQ0DSZM206GPQMH5NT2
- Published
- 11 Sept 2026
- T1 · 34 min ago
- arXiv
- 2609.10589
- T1 · 34 min ago
- Category
- cs.LG
- T1 · 34 min ago
Abstract
Heteroscedastic forecasting, where a network estimates a scale parameter alongside a location parameter, is normally motivated by uncertainty quantification. This paper shows it also improves the point estimate, in contrast to reported negative results for heteroscedastic estimation outside time series. Halo is a modification that reuses an existing deep forecaster's architecture, giving it a second output for the scale of its implied distribution and training it under the matching negative log likelihood. Adapting three state-of-the-art models --- a transformer, a graph network paired with a variational autoencoder, and a single-layer convolutional network --- under both Gaussian and Laplacian losses demonstrates the phenomenon. On the five electricity price markets of a standard forecasting benchmark, Halo improves MSE and MAE in 28 of 30 model-market-metric comparisons, cutting average MSE by 2.6% to 16.5% and average MAE by 1.7% to 11.0%. Two findings emerge: (1) whether the scale estimate comes from a second projection head or from a full parallel network matters far less than whether the network estimates scale, and (2) the improvement holds under the hyperparameters already tuned for the point-estimate baseline, so retuning is optional.
Authors 1
Adam Cataldo
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- arXiv id
- 2609.10589
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Categories
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
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Attributed facts
9
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T19
Freshest observation
34 min ago
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- Authors
- Adam Cataldo
As of
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| Heteroscedastic forecasting, where a network estimates a scale parameter alongside a location parameter, is normally motivated by uncertainty quantification. This paper shows it also improves the point estimate, in contrast to reported negative results for heteroscedastic estimation outside time series. Halo is a modification that reuses an existing deep forecaster's architecture, giving it a second output for the scale of its implied distribution and training it under the matching negative log likelihood. Adapting three state-of-the-art models --- a transformer, a graph network paired with a variational autoencoder, and a single-layer convolutional network --- under both Gaussian and Laplacian losses demonstrates the phenomenon. On the five electricity price markets of a standard forecasting benchmark, Halo improves MSE and MAE in 28 of 30 model-market-metric comparisons, cutting average MSE by 2.6% to 16.5% and average MAE by 1.7% to 11.0%. Two findings emerge: (1) whether the scale estimate comes from a second projection head or from a full parallel network matters far less than whether the network estimates scale, and (2) the improvement holds under the hyperparameters already tuned for the point-estimate baseline, so retuning is optional. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: Halo: Improving forecast accuracy through heteroscedastic estimation
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 34 min ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.