时间序列预测的深度学习-重点是损失函数和误差测量

Sujeeth R Malhathkar, T. S
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引用次数: 13

摘要

时间序列预测是现代世界的一项重要任务,在许多领域都有应用。由于与标准统计模型相比,深度学习模型具有优越的性能,因此在过去几年中,使用深度学习模型进行预测越来越受欢迎。研究了2018-2020年期间使用深度学习模型进行预测的13个此类模型的独特方法。这项工作特别关注模型训练期间损失函数的使用,以及用于评估模型并将其与其他模型进行比较的误差度量的使用。我们已经观察到,尽管损失函数在训练过程中发挥作用,但它们不像其他训练参数那样被严格对待,并且在使用误差指标报告模型性能时存在几个问题。提出了存在的问题和建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning for Time Series Forecasting – With a focus on Loss Functions and Error Measures
Time Series Forecasting is a significant task in the modern world that finds application in several fields. The use of Deep Learning Models to perform forecasting has gained popularity in the past few years due to their superior performance in comparison to standard statistical models. 13 such models were unique in their approach to forecasting using Deep Learning models in the period 2018-2020 are studied. This work lays special focus on the use of loss function during model training and the use of error measures used to evaluate a model and compare the same with other models. We have observed that although loss functions play a role in the training process, they are not treated as rigorously as other training parameters, and several issues when it comes to reporting model performance using error metrics. The problems are highlighted and suggestions are listed.
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