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Pronóstico robusto de series temporales multivariadas contra el desplazamiento de transición entre series e

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    Este estudio presenta JointPGM, un nuevo modelo gráfico probabilístico para abordar el cambio de distribución en el pronóstico de series temporales multivariadas (MTS). JointPGM captura efectivamente correlaciones complejas y dinámicas que varían en el tiempo para mejorar la precisión de los pronósticos.

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    Área de la Ciencia:

    • Aprendizaje automático
    • Análisis de las series temporales
    • Ciencia de los datos

    Sus antecedentes:

    • Los datos de series temporales multivariadas (MTS) del mundo real muestran no estacionalidad, lo que causa un cambio de distribución que desafía los modelos de pronóstico.
    • Los métodos existentes como la normalización adaptativa y el modelado de variantes de tiempo tienen limitaciones en la captura de correlaciones intraseries / interseries y las causas fundamentales del cambio de distribución.

    Objetivo del estudio:

    • Desarrollar un modelo gráfico probabilístico unificado (PGM) para abordar conjuntamente las correlaciones intraseries/interseries y las distribuciones de variantes de tiempo en las previsiones MTS no estacionarias.
    • Introducir un marco neuronal, JointPGM, diseñado para mitigar las limitaciones de los enfoques actuales para la previsión de MTS.

    Principales métodos:

    • JointPGM utiliza las funciones de base de Fourier para aprender los factores de tiempo dinámicos.
    • Incorpora aprendices intraseries e interseries distintos para capturar las dinámicas temporales y espaciales, respectivamente.
    • El muestreo Gumbel-softmax y la propagación multihop se emplean para el modelado explícito de la dinámica espacial.

    Principales resultados:

    • JointPGM logra un rendimiento de pronóstico de última generación (SOTA) en seis conjuntos de datos MTS altamente no estacionarios.
    • El modelo demuestra eficacia y eficiencia en el manejo de dinámicas complejas temporales y espaciales.
    • La validación experimental confirma la capacidad del modelo para capturar las causas subyacentes del cambio de distribución.

    Conclusiones:

    • JointPGM ofrece un marco unificado para la previsión MTS no estacionaria mediante el modelado conjunto de correlaciones y distribuciones de variantes de tiempo.
    • El marco neuronal propuesto mejora la expresividad y la interpretabilidad del modelo para abordar el cambio de distribución.
    • Los resultados ponen de relieve el potencial de JointPGM para mejorar la precisión y la robustez de las previsiones MTS.