Взвешенная ансамблевая модель, сочетающая ARIMA, LSTM и GBM для надежного прогнозирования временных рядов

   Прогнозирование временных рядов используется в исследованиях и приложениях в ряде областей, таких как прогнозирование состояния окружающей среды, здравоохранение, финансы, управление цепочками поставок и энергопотребление. Точное прогнозирование будущих значений необходимо для стратегического планирования, эффективности работы и принятия обоснованных решений относительно переменных, зависящих от времени. Предлагается гибридная архитектура прогнозирования временных рядов, сочетающая в себе сильные стороны машинного обучения и статистических моделей, в частности сетей градиентного бустинга (Gradient Boosting Machines, GBM), авторегрессионных интегрированных скользящих средних (Auto-Regressive Integrated Moving Average, ARIMA) и сетей с долговременной краткосрочной памятью (Long Short-Term Memory, LSTM). В то время как сети LSTM и GBM способны улавливать сложные и нелинейные закономерности, модель ARIMA фиксирует линейные компоненты временных рядов. Гибридная модель использует интерпретируемость ARIMA, временную память LSTM и эффективность ансамблевого обучения GBM, интегрируя эти три модели. Комплексные эксперименты, проведенные на эталонных наборах данных, показали, что точность и надежность прогнозов предлагаемой гибридизации значительно превосходят как отдельные модели, так и традиционные базовые. Результаты показывают, что для различных реальных приложений гибридные архитектуры могут обеспечивать надежные и точные прогнозы временных рядов.

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