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

arXiv: https://arxiv.org/abs/2203.11196 · HTML: https://mlsanity.com/paper/2203.11196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Meta-learning framework with applications to zero-shot time-series forecasting](https://mlsanity.com/paper/2002.02887/) (2020)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/1905.13628/) (2019)
- [Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks](https://mlsanity.com/paper/1904.00655/) (2019)
- [Energy Predictive Models with Limited Data using Transfer Learning](https://mlsanity.com/paper/1906.02646/) (2019)
- [Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?](https://mlsanity.com/paper/2201.04449/) (2022)
- [A network-based transfer learning approach to improve sales forecasting of new products](https://mlsanity.com/paper/2005.06978/) (2020)
- [A Meta-learning based Distribution System Load Forecasting Model Selection Framework](https://mlsanity.com/paper/2009.12001/) (2020)

## Abstract (arXiv)

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.