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.

翻译：深度学习和迁移学习模型被用于生成时间序列预测，但关于其预测性能的证据仍然稀缺，尤其是在月度时间序列中更为明显。本文旨在比较带迁移学习与不带迁移学习的深度学习模型以及其他传统方法在月度预测中的表现，以回答关于深度学习和迁移学习是否适合生成时间序列预测的三个问题。实验使用了M4和M3竞赛的时间序列数据。结果表明，基于TCN、LSTM和CNN的深度学习模型结合迁移学习，其预测性能往往优于其他传统方法。另一方面，直接针对目标时间序列训练的TCN和LSTM模型，在某些预测时间跨度上的表现与传统方法相近或更优。