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.

通过对M3和M4竞赛中的时间序列进行实验，比较了基于TCN、LSTM和CNN的深度学习模型和其他传统方法的性能预测。结果表明，使用迁移学习的深度学习模型往往优于传统方法；对于某些预测范围，直接在目标时间序列上训练的TCN和LSTM的表现与传统方法相当甚至更好。

基于转移学习的深度学习模型在月度时间序列多步预测中的性能