Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
Deep learning techniques have significantly advanced time series prediction by effectively modeling temporal dependencies, particularly for datasets with numerous observations. Although larger datasets are generally associated with improved accuracy, the results of this study demonstrate that this a...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/11081 |
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| author | Ana Lazcano Pablo Hidalgo Julio E. Sandubete |
| author_facet | Ana Lazcano Pablo Hidalgo Julio E. Sandubete |
| author_sort | Ana Lazcano |
| collection | DOAJ |
| description | Deep learning techniques have significantly advanced time series prediction by effectively modeling temporal dependencies, particularly for datasets with numerous observations. Although larger datasets are generally associated with improved accuracy, the results of this study demonstrate that this assumption does not always hold. By progressively increasing the amount of training data in a controlled experimental setup, the best predictive metrics were achieved in intermediate iterations, with variations of up to 66% in RMSE and 44% in MAPE across different models and datasets. The findings challenge the notion that more data necessarily leads to better generalization, showing that additional observations can sometimes result in diminishing returns or even degradation of predictive metrics. These results emphasize the importance of strategically balancing dataset size and model optimization to achieve robust and efficient performance. Such insights offer valuable guidance for time series forecasting, especially in contexts where computational efficiency and predictive accuracy must be optimized. |
| format | Article |
| id | doaj-art-963f426837ef49558b91e6f19eb4e2d2 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-963f426837ef49558b91e6f19eb4e2d22024-12-13T16:22:45ZengMDPI AGApplied Sciences2076-34172024-11-0114231108110.3390/app142311081Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep LearningAna Lazcano0Pablo Hidalgo1Julio E. Sandubete2Faculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 Madrid, SpainFaculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 Madrid, SpainFaculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 Madrid, SpainDeep learning techniques have significantly advanced time series prediction by effectively modeling temporal dependencies, particularly for datasets with numerous observations. Although larger datasets are generally associated with improved accuracy, the results of this study demonstrate that this assumption does not always hold. By progressively increasing the amount of training data in a controlled experimental setup, the best predictive metrics were achieved in intermediate iterations, with variations of up to 66% in RMSE and 44% in MAPE across different models and datasets. The findings challenge the notion that more data necessarily leads to better generalization, showing that additional observations can sometimes result in diminishing returns or even degradation of predictive metrics. These results emphasize the importance of strategically balancing dataset size and model optimization to achieve robust and efficient performance. Such insights offer valuable guidance for time series forecasting, especially in contexts where computational efficiency and predictive accuracy must be optimized.https://www.mdpi.com/2076-3417/14/23/11081time series forecastingpreprocessingMLPLSTMtransformer |
| spellingShingle | Ana Lazcano Pablo Hidalgo Julio E. Sandubete Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning Applied Sciences time series forecasting preprocessing MLP LSTM transformer |
| title | Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning |
| title_full | Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning |
| title_fullStr | Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning |
| title_full_unstemmed | Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning |
| title_short | Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning |
| title_sort | walking back the data quantity assumption to improve time series prediction in deep learning |
| topic | time series forecasting preprocessing MLP LSTM transformer |
| url | https://www.mdpi.com/2076-3417/14/23/11081 |
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