论文标题
通过转移学习的深度学习模型的性能，用于每月时间序列中的多步预测
Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series
论文作者
论文摘要
深度学习和转移学习模型用于生成时间序列的预测。但是，有稀缺的证据表明他们的表现预测，每月时间序列更为明显。本文的目的是将深度学习模型与转移学习，不转移学习和其他用于每月预测的传统方法进行比较，以回答有关深度学习和转移学习的适用性以生成时间序列预测的三个问题。 M4和M3竞赛的时间序列用于实验。结果表明，基于转移学习的基于TCN，LSTM和CNN的深度学习模型往往会超过其他传统方法的性能预测。另一方面，直接在目标时间序列上训练的TCN和LSTM比传统方法在某些预测范围内具有相似或更好的性能。
Deep Learning and transfer learning models are being used to generate time series forecasts; however, there is scarce evidence about their performance prediction that it is more evident for monthly time series. The purpose of this paper is to compare Deep Learning models with transfer learning and without transfer learning and other traditional methods used for monthly forecasts to answer three questions about the suitability of Deep Learning and Transfer Learning to generate predictions of time series. Time series of M4 and M3 competitions were used for the experiments. The results suggest that deep learning models based on TCN, LSTM, and CNN with transfer learning tend to surpass the performance prediction of other traditional methods. On the other hand, TCN and LSTM, trained directly on the target time series, got similar or better performance than traditional methods for some forecast horizons.
