Explaining When Deep Learning Models Are Better for Time Series Forecasting

Martín Solís; Luis Alexander Calvo-Valverde

doi:10.3390/engproc2024068001

Explaining When Deep Learning Models Are Better for Time Series Forecasting

Título traducido de la contribución: Explicación de cuándo los modelos de aprendizaje profundo son mejores para la predicción de series temporales

Martín Solís, Luis Alexander Calvo-Valverde

Producción científica: Contribución a una revista › Artículo de la conferencia › revisión exhaustiva

4 Citas (Scopus)

Resumen

There is a gap of knowledge about the conditions that explain why a method has a better forecasting performance than another. Specifically, this research aims to find the factors that can influence deep learning models to work better with time series. We generated linear regression models to analyze if 11 time series characteristics influence the performance of deep learning models versus statistical models and other machine learning models. For the analyses, 2000 time series of M4 competition were selected. The results show findings that can help explain better why a pretrained deep learning model is better than another kind of model.

Título traducido de la contribución	Explicación de cuándo los modelos de aprendizaje profundo son mejores para la predicción de series temporales
Idioma original	Inglés
Número de artículo	1
Páginas (desde-hasta)	1-8
Número de páginas	8
Publicación	Engineering Proceedings
Volumen	68
N.º	1
DOI	https://doi.org/10.3390/engproc2024068001 https://doi.org/10.3390/engproc2024068001
Estado	Publicada - 27 jun 2024

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abstract = "There is a gap of knowledge about the conditions that explain why a method has a better forecasting performance than another. Specifically, this research aims to find the factors that can influence deep learning models to work better with time series. We generated linear regression models to analyze if 11 time series characteristics influence the performance of deep learning models versus statistical models and other machine learning models. For the analyses, 2000 time series of M4 competition were selected. The results show findings that can help explain better why a pretrained deep learning model is better than another kind of model.",

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AB - There is a gap of knowledge about the conditions that explain why a method has a better forecasting performance than another. Specifically, this research aims to find the factors that can influence deep learning models to work better with time series. We generated linear regression models to analyze if 11 time series characteristics influence the performance of deep learning models versus statistical models and other machine learning models. For the analyses, 2000 time series of M4 competition were selected. The results show findings that can help explain better why a pretrained deep learning model is better than another kind of model.

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Explaining When Deep Learning Models Are Better for Time Series Forecasting

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