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Meta Controller: Meta-Task & Meta-Dataset & Meta-Model

qlib

Introduction

Meta Controller provides guidance to Forecast Model, which aims to learn regular patterns among a series of forecasting tasks and use learned patterns to guide forthcoming forecasting tasks. Users can implement their own meta-model instance based on Meta Controller module.

Meta Task

A Meta Task instance is the basic element in the meta-learning framework. It saves the data that can be used for the Meta Model. Multiple Meta Task instances may share the same Data Handler, controlled by Meta Dataset. Users should use prepare_task_data() to obtain the data that can be directly fed into the Meta Model.

qlib.model.meta.task.MetaTask

Meta Dataset

Meta Dataset controls the meta-information generating process. It is on the duty of providing data for training the Meta Model. Users should use prepare_tasks to retrieve a list of Meta Task instances.

qlib.model.meta.dataset.MetaTaskDataset

Meta Model

General Meta Model

Meta Model instance is the part that controls the workflow. The usage of the Meta Model includes: 1. Users train their Meta Model with the fit function. 2. The Meta Model instance guides the workflow by giving useful information via the inference function.

qlib.model.meta.model.MetaModel

Meta Task Model

This type of meta-model may interact with task definitions directly. Then, the Meta Task Model is the class for them to inherit from. They guide the base tasks by modifying the base task definitions. The function prepare_tasks can be used to obtain the modified base task definitions.

qlib.model.meta.model.MetaTaskModel

Meta Guide Model

This type of meta-model participates in the training process of the base forecasting model. The meta-model may guide the base forecasting models during their training to improve their performances.

qlib.model.meta.model.MetaGuideModel

Example

Qlib provides an implementation of Meta Model module, DDG-DA, which adapts to the market dynamics.

DDG-DA includes four steps:

  1. Calculate meta-information and encapsulate it into Meta Task instances. All the meta-tasks form a Meta Dataset instance.
  2. Train DDG-DA based on the training data of the meta-dataset.
  3. Do the inference of the DDG-DA to get guide information.
  4. Apply guide information to the forecasting models to improve their performances.

The above example can be found in examples/benchmarks_dynamic/DDG-DA/workflow.py.