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Adapformer: gestión de canales adaptativos para el pronóstico de series temporales multivariadas

Yuchen Luo¹, Xinyu Li², Liuhua Peng¹

¹School of Mathematics and Statistics, The University of Melbourne, Melbourne, Parkville, VIC 3052, Australia.

Neural networks : the official journal of the International Neural Network Society

|September 5, 2025

Resumen

Este estudio presenta Adapformer, un nuevo enfoque para el pronóstico de series temporales multivariadas (MTSF). Adapformer modela efectivamente las dependencias complejas, superando a los métodos existentes en precisión y eficiencia.

Palabras clave:

Gestión de canales Datos multivariados Pronóstico selectivo Pronóstico de las series temporales El transformador

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Área de la Ciencia:

Inteligencia artificial
Aprendizaje automático
Análisis de las series temporales

Sus antecedentes:

El pronóstico de series temporales multivariadas (MTSF) se enfrenta a desafíos en el modelado de dependencias entre variables.
Los métodos existentes independientes del canal (CI) y dependientes del canal (CD) tienen limitaciones, ya sea ignorando las interacciones o introduciendo ruido.
Se necesitan modelos avanzados de MTSF que equilibren la captura de dependencias con la eficiencia predictiva.

Objetivo del estudio:

Introducir el transformador de pronóstico adaptativo (Adapformer), un nuevo marco para el MTSF.
Abordar las limitaciones de los enfoques de CI y CD mediante la integración de una gestión eficaz de los canales.
Mejorar tanto la precisión como la eficiencia computacional de las series temporales multivariadas.

Principales métodos:

Desarrolló Adapformer, un marco basado en Transformer con una arquitectura de codificador-decodificador de dos etapas.
Se introdujo el Adaptive Channel Enhancer (ACE) para enriquecer las representaciones de tokens mediante la incorporación selectiva de dependencias.
Implementó el pronóstico de canal adaptativo (ACF) para refinar las predicciones centrándose en las covariables relevantes, reduciendo el ruido.

Principales resultados:

Adapformer demostró un rendimiento superior en comparación con los modelos MTSF existentes en diversos conjuntos de datos.
El modelo propuesto logró una mayor precisión predictiva.
Se observaron mejoras significativas en la eficiencia computacional con Adapformer.

Conclusiones:

Adapformer ofrece una solución de última generación para MTSF mediante la gestión eficaz de las dependencias de canal.
El marco combina con éxito los beneficios de las estrategias de IC y CD.
Adapformer representa un avance significativo en el pronóstico preciso y eficiente de series temporales multivariadas.