LLM Driven AutoForecasting with Sktime's `Craft()`
The article discusses the integration of large language models (LLMs) with the sktime library's `craft()` method for time series forecasting. It introduces the `LLMBlueprintForecaster`, which utilizes LLMs to generate and refine forecasting pipelines. The author highlights the advantages of using LLMs for hyperparameter selection and the iterative process of improving forecasting models.
- ▪The `craft()` method in sktime allows for the creation of estimators and pipelines from text strings formatted like Python code.
- ▪The `LLMBlueprintForecaster` uses LLMs to propose new forecasting models based on previous iterations and evaluation results.
- ▪This approach aims to automate the selection of the best forecasting model for time series data, enhancing the efficiency of the forecasting process.
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| Original publisher | Medium |
| Canonical URL | https://pub.towardsai.net/llm-driven-autoforecasting-with-sktimes-craft-0355f5c720e8 |
| Publication time | Tue, 26 May 2026 14:32:54 +0000 |
| Retrieval time | 2026-05-26T14:37:49.954Z |
| Last seen | 2026-05-26T14:37:49.954Z |
| 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 | TbkHBhOGNTjc |
| 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 |
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| 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
LLM Driven AutoForecasting with Sktime’s `craft()`Benedikt Heidrich8 min read·18 hours ago--ListenSharePress enter or click to view image in full sizeGraphical Abstract: An LLM is proposing blueprints, these are passed to the sktime’s craft method and evaluated iteratively during fit. Predict is using the best estimator found during fit to create the forecast.AutoML is relying in many cases on some kind of grid searches. This is expensive. However, if humans are selecting hyperparameters, they often have some feeling about a good choice based on previous experiences. So I wondered, if LLM can act like an human expert and determine the parameters by considering the problem and using the knowledge stored in its weights.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.
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