Foundational model

As explained in Time Series Forecasting Using Foundation Models

A foundation model is a machine learning model trained on large, diverse data and applicable to a wide variety of tasks, often through adaptation or fine-tuning.

A foundational model is a large machine-learning model trained on large, diverse data and usable across many tasks. It can often be adapted or fine-tuned for a particular scenario. Unlike a data-specific model, it is intended for reuse across datasets and use cases. section 1.1

Figure 1.1 The result of performing linear regression on two different datasets. Although the algorithm used to build the linear model stays the same, the model is different depending on the dataset used.

Linear regression uses the same algorithm on two datasets but produces different models, illustrating how training data shapes a model. section 1.1

Foundational models shift work from building one model per task toward loading a reusable model and applying it immediately. In time-series forecasting, one model may handle different frequencies, seasonality, holiday effects, multiple series, anomaly detection, or classification. Derivatives of the transformer architecture power many such models. section 1.0 section 1.5

Evaluate the approach against the demands of the forecasting problem:

• Use zero-shot forecasting when the model predicts a new dataset without training on it; fine-tune when a few training steps can specialize it to the task. section 2.11
• Prefer it when data is scarce, rapid deployment matters, or one model must be reused across many datasets. section 1.3.1
• Check frequency, forecast horizon, multivariate support, exogenous features, privacy, hardware, and infrastructure before adopting it. section 1.3.2 section 2.9
• Compare its predictions with data-specific alternatives using explicit metrics rather than assuming general training guarantees the best result. section 1.3.2 section 2.8

Daily temperature forecasting

A tiny N-BEATS model pretrained only on monthly data produced weaker zero-shot forecasts for daily Australian temperatures than a model trained specifically on the daily dataset. The zero-shot model achieved MAE 2.59 and sMAPE 8.69%, versus 1.34 and 4.87% for the data-specific model. section 2.8

How Time Series Forecasting Using Foundation Models covers it

How deep it goes

Defines large-scale pretraining, reuse, zero-shot prediction, and fine-tuning, then evaluates frequency, horizon, capability, privacy, and resource limits.

Examples

Uses N-BEATS, TimeGPT, Lag-Llama, Chronos, Moirai, TimesFM, PromptCast, and Time-LLM for forecasting experiments and comparisons.

What it assumes

Assumes basic programming skills for initial use while teaching pretraining, transfer learning, zero-shot prediction, and fine-tuning through smaller models.

What you can do after

Evaluate and apply foundation forecasting models through zero-shot prediction, fine-tuning, API or package access, and metric-based comparison.

How it connects

Connects pretraining with transfer learning, fine-tuning, zero-shot forecasting, transformer architecture, time-series forecasting, anomaly detection, and exogenous features.

liveBookmanning.com

Read Time Series Forecasting Using Foundation Models on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsA model whose parameters were fitted on a source task or dataset before reuse.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA model trained beforehand on large-scale data and then reused for downstream tasks or analysis without starting from scratch.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionA foundation model is a large pretrained model intended to support many downstream tasks with little or no task-specific retraining.
• 

  liveBookmanning.com
  AI-Powered SearchA pretrained model is a model that has already been trained on general data before adaptation to a particular task or domain.
• 

  liveBookmanning.com
  Generative AI in ActionA pretrained model is a machine-learning model that has already been trained on an initial dataset before being adapted to another task or dataset.
• 

  liveBookmanning.com
  Deep Learning with PyTorch, Second EditionA neural network already trained on prior data and reused for inference or further training.
• 

  liveBookmanning.com
  Machine Learning System DesignA machine learning model whose parameters were learned previously on a dataset or task before being applied or adapted to another task.
• 

  liveBookmanning.com
  Delivering Machine Learning Projects: From design to deploymentA foundation model is a broadly trained machine learning model that can be adapted to support multiple downstream tasks.
• 

  liveBookmanning.com
  LLMs in ProductionA foundation model is a broadly pretrained model that can be adapted to multiple downstream tasks or domains.
• 

  liveBookmanning.com
  How Large Language Models WorkA foundation model is a broadly trained model that can be adapted or used as a basis for many applications.
• 

  liveBookmanning.com
  Hugging Face in ActionA pretrained model is a machine learning model whose parameters have already been learned from a training dataset before being adapted or used for a particular task.
• 

  liveBookmanning.com
  Effective Data AnalysisA foundation model is a broadly pretrained model that can be adapted or prompted for many downstream tasks and domains.
• 

  liveBookmanning.com
  The AI Pocket BookA preexisting model used as-is or with minimal adaptation instead of building one from scratch.
• 

  liveBookmanning.com
  Reinforcement Learning from Human FeedbackA foundation model is a broadly pretrained model that can be adapted to support multiple downstream tasks.
• 

  liveBookmanning.com
  Software Testing with Generative AIA foundational model is a pretrained machine-learning model that provides a general base for further adaptation or specialization.
• 

  liveBookmanning.com
  Natural Language Processing in Action, Second EditionA pretrained model is a model whose parameters were previously learned from a separate training corpus or task.
• 

  liveBookmanning.com
  Transformers in ActionA pretrained model is a machine-learning model whose parameters have already been learned from a broad training corpus before adaptation to a specific task.
• 

  liveBookmanning.com
  Deep Learning with JAXA model that has already been trained on a large corpus and can be reused directly or adapted to a new task.
• 

  liveBookmanning.com
  The Art of AI Product DevelopmentA foundational model is a broadly trained model that can be adapted to support many downstream tasks and applications.
• 

  liveBookmanning.com
  Financial AI in PracticeA foundation model is a large, broadly trained model that can be adapted or guided for many downstream tasks.

Compare how 21 books explain foundational model →

Related concepts

• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• pretrained modelkind of · A pretrained model is a machine-learning model whose parameters were learned through prior training before it is used or adapted for a subsequent task.
• instruct modelincludes · An instruction-tuned model is a pretrained model further trained to follow natural-language instructions and produce task-appropriate responses.
• feature engineeringused by · Feature engineering is the creation, transformation, or selection of input features from data to provide useful representations for analysis or modeling.
• TimesFMincludes · TimesFM is a foundation model designed for deterministic time-series forecasting.

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.