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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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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