Chronos vs Toto: Zero-Shot Forecasting Benchmark Results
The article compares two forecasting models, Chronos and Toto, using telemetry data from Prometheus and OpenSearch. It evaluates their performance in a zero-shot setting, emphasizing the importance of accurate long-horizon forecasts for capacity planning. The analysis highlights the challenges of forecasting in observability due to the variability and unpredictability of real systems.
- ▪Chronos and Toto were tested on telemetry data to assess their forecasting capabilities.
- ▪The evaluation focused on point accuracy and the quality of uncertainty in forecasts.
- ▪Prometheus memory utilization showed stable behavior, while OpenSearch CPU demonstrated high volatility and frequent outliers.
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/team-parseable/chronos-vs-toto-zero-shot-forecasting-benchmark-results-1101 |
| Publication time | Wed, 27 May 2026 04:41:57 +0000 |
| Retrieval time | 2026-05-27T05:07:56.817Z |
| Last seen | 2026-05-27T05:07:56.817Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0UJqaIPWxdpZ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3924679) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Parseable Team Posted on May 27 • Originally published at parseable.com Chronos vs Toto: Zero-Shot Forecasting Benchmark Results #chronos #dataengineering #observability Introduction Good forecasts help with capacity planning and quieter alerts. But one traffic spike or memory leak can make any forecast useless. The goal is simple: prove your forecast beats a naive baseline and stays reliable under uncertainty.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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