Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series. (arXiv:2203.11196v1 [cs.LG])

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.



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