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Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series

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Martín Solís, Luis-Alexander Calvo-Valverde (Tecnológico de Costa Rica)

Machine Learning · Submitted 18 Mar 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · hedged possibility 0.99 read the record →

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arXiv ID2203.11196 (version 2)
Pith ID4OWIBIM5, Pith's permanent record of this paper and its review
CitationLuis-Alexander Calvo-Valverde (Tecnol\'ogico de Costa Rica), Mart\'in Sol\'is. "Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series." arXiv:2203.11196 (2022). https://pith.science/paper/4OWIBIM5
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The compiled record

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,290 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.37 · human participants 0.02 · controlled comparison 0.91 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.94 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. gap 0.78 0.05 0.03 asserted 0.77
s1 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. contribution 1.00 0.41 0.03 asserted 0.66
s2 Time series of M4 and M3 competitions were used for the experiments. method 1.00 0.84 0.02 asserted 0.97
s3 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. result 0.96 0.91 0.71 hedged possibility 0.99
s4 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. result 1.00 0.93 0.12 asserted 0.75

What kind of paper the classifier reports this is: empirical study 0.99 · evidence from observation or experiment 0.73.

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  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • T body sentences
  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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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