INSIGHTS: Demonstration-Based Summaries of Time Series Predictors
The paper introduces INSIGHTS, a model-agnostic approach for providing global explanations of time series models. It emphasizes simplicity, efficiency, and transparency, allowing stakeholders to easily understand model behavior. The evaluation shows that INSIGHTS effectively generates informative summaries that enhance users' comprehension of time series data.
- ▪INSIGHTS focuses on global explanations rather than local, instance-level attributions.
- ▪The approach generates sample summaries that provide a comprehensive overview of model behavior.
- ▪User studies indicate that INSIGHTS-based summaries improve understanding of the model's overall behavior.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18849 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| 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 | G5Fjc4Cql4GH |
| 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
Computer Science > Machine Learning arXiv:2605.18849 (cs) [Submitted on 13 May 2026] Title:INSIGHTS: Demonstration-Based Summaries of Time Series Predictors Authors:Bar Eini Porat, Rom Gutman, Uri Shalit, Ofra Amir View a PDF of the paper titled INSIGHTS: Demonstration-Based Summaries of Time Series Predictors, by Bar Eini Porat and 2 other authors View PDF HTML (experimental) Abstract:Explainability methods have progressed rapidly, but global explanations for time-series models remain underdeveloped, with most approaches focusing on local, instance-level attributions. We introduce INSIGHTS, a model-agnostic, user-centric approach for providing global explanations of time series models.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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