# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 7 Conclusions  
The results of this research allow us to conclude that, contrary to what is commonly assumed, a greater number of observations does not necessarily guarantee an improvement in error metrics when training recurrent neural networks, such as MLPs, LSTMs, and transformers, for time series prediction. The best metrics were obtained at different stages of the iterations, depending on the specific characteristics of each series, indicating that the optimal number of observations varies from case to case.  
In addition, no evidence of a significant improvement has been found when including more training data in terms of predictive performance, causing in some of the series a significant worsening of the resulting metrics, even leading to an underfitting of the model. This methodology allows significant computational savings by identifying the ideal amount of data for training and excessive training time. Furthermore, the metrics calculated for the training and testing partitions demonstrate that the methodology avoids both underfitting and overfitting.  
The practical implications of these findings are significant: it is possible to reduce the number of observations used to train neural networks without losing precision in the predictions, which could optimize training time and reduce the computational resources required. This has a direct impact on the development of real-time forecasting systems or in environments where resources are limited.  
Finally, the future implications of this research suggest that it is necessary to develop more accurate techniques to automatically determine the optimal number of observations based on the specific characteristics of the time series and the model used. Reducing redundant or irrelevant data without sacrificing accuracy is a key area for further research.  
**Author Contributions:** Conceptualization, A.L., P.H. and J.E.S.; methodology, A.L.; software, A.L.; validation, A.L.; investigation, A.L., P.H. and J.E.S.; resources, A.L., P.H. and J.E.S.; data curation, A.L., P.H. and J.E.S.; writing--original draft preparation, A.L., P.H. and J.E.S.; writing--review and editing, A.L., P.H. and J.E.S.; visualization, A.L., P.H. and J.E.S.; supervision, A.L., P.H. and J.E.S.; project administration, A.L., P.H. and J.E.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Instructional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from Thomson Eikon Reuters and are available from the Refinitiity application with the permission of Thomson Eikon Reuters. Code Available at: [[https://colab.research.google.com/drive/1j5p8](https://colab.research.google.com/drive/1j5p8) SRrIHz7 dPhCwV1 dYfetLKYnsUP?usp=sharing]([https://colab.research.google.com/drive/1j5p8](https://colab.research.google.com/drive/1j5p8) SRrIHz7 dPhCwV1 dYfetLKYnsUP?usp=sharing) (accessed on 19 May 2024).  
**Conflicts of Interest:** The authors declare no conflict of interest.