# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 6 Discussion  
The results demonstrate that in neural networks, optimal performance is not necessarily tied to the volume of observations in the training set. The findings reveal that the best error metrics vary across different iterations, depending on the characteristics of each time series, indicating that more data does not universally enhance model accuracy. This aligns with previous studies, which suggest that after a certain threshold, adding additional observations does not yield significant improvements in predictive accuracy and may even lead to diminishing returns. Unlike research focused on sparse datasets, where adding synthetic data often helps mitigate high error metrics (as shown by [74; 75]), this study highlights that when sufficient data are available, the inclusion of extra observations can sometimes increase error rather than reduce it. This finding challenges the assumption that increasing data volume always enhances model performance, especially for datasets with complex, non-linear temporal dependencies. The current literature reflects no clear consensus on the ideal quantity of observations for training time series models [76; 77; 78]. While some studies advocate for larger datasets, others, such as [79; 80], report that increasing the number of observations does not necessarily improve error metrics, resonating with the findings. This study adds to this discussion by showing that after a certain threshold, additional data points may not reduce error and, in some cases, may even degrade performance. This pattern is consistent with research by [81], who observed that after reaching a certain number of observations, the error metrics plateaued while computational costs continued to rise. Thus, the results suggest that the relationship between dataset size and neural network performance is non-linear, and that excessive data may impose additional computational burdens without corresponding accuracy gains. Another important implication of the findings is the potential to enhance computational efficiency. The observation that error rates do not decrease significantly after a certain dataset size suggests that training times could be reduced without compromising model accuracy. This has practical value for applications requiring real-time predictions or those operating under limited computational resources, as it supports the notion that an optimal subset of observations can achieve near-maximum performance while reducing training costs. Such optimization could be particularly beneficial in large-scale prediction systems, where resource allocation and training efficiency are critical factors. By identifying an optimal balance between data volume and accuracy, the approach provides a framework to refine both predictive performance and associated computational expenses. In a broader context, these findings offer practical guidelines for handling time series prediction problems across various domains, such as finance, healthcare, and environmental forecasting, where data may either be abundant or constrained by factors like data privacy or collection limitations. By leveraging an optimal subset of observations, practitioners can achieve competitive model performance while minimizing computational costs, which is particularly advantageous in real-time forecasting and low-resource environments. Additionally, this approach allows for more sustainable model deployment by reducing energy consumption associated with training on excessive data. As a result, the methodology contributes to a more efficient, scalable approach to time series forecasting that can be tailored to the specific data availability and computational constraints of diverse applications. Nonetheless, it is important to acknowledge certain limitations of this study. First, the observed behavior varied depending on the specific time series and neural network model employed.