Pretrained model

Q: How deeply does Time Series Forecasting Using Foundation Models cover pretrained model?

Explains pretraining, zero-shot prediction, and fine-tuning, but does not detail parameter updates or optimization mechanics.

As explained in Time Series Forecasting Using Foundation Models

A pretrained model is a model whose parameters were learned on earlier data before its application to a new task or dataset.

A pretrained model has learned general patterns from earlier data and can be applied to a new task or dataset through transfer learning. section 2.11 section 2.3

Apply a pretrained model in two main ways:

Generate zero-shot predictions without training on the new dataset. section 2.11
Fine-tune it for a few steps so it specializes in the target task. section 2.11
Use it when the target dataset is too small to support training a model from scratch. section 2.3

Forecasting a new product

A pretrained forecasting model can predict sales for a new product before enough historical observations exist to train a product-specific model. section 2.3

How Time Series Forecasting Using Foundation Models covers it

Cover of Time Series Forecasting Using Foundation Models

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Related concepts

foundational modelincludes · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
SAMincludes · SAM, or Segment Anything Model, is a pretrained promptable computer-vision model that generates segmentation masks for objects in images.
fine-tuningused by · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
pretrainingproduced by · Pretraining is the initial training of a model on broad data to learn general patterns or capabilities before adaptation to specific tasks or domains.
transfer learningused by · Transfer learning is a machine-learning technique that reuses knowledge learned from one task, domain, or dataset to improve learning or performance on another.
feature engineeringuses · Feature engineering is the creation, transformation, or selection of input features from data to provide useful representations for analysis or modeling.

This explainer was generated by Manning's knowledge graph from the text of Time Series Forecasting Using Foundation Models. The section links point to the parts of the book it draws on.