Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports
Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals,...
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MDPI AG
2024-08-01
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| author | Unyamanee Kummaraka Patchanok Srisuradetchai |
| author_facet | Unyamanee Kummaraka Patchanok Srisuradetchai |
| author_sort | Unyamanee Kummaraka |
| collection | DOAJ |
| description | Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals, especially for time series with trends and seasonal patterns. This paper explicitly details a practical approach integrating dual-output Monte Carlo Dropout (MCDO) with DNNs to approximate predictive means and variances within a Bayesian framework, enabling forecast interval construction. The dual-output architecture employs a custom loss function, combining mean squared error with Softplus-derived predictive variance, ensuring non-negative variance values. Hyperparameter optimization is performed through a grid search exploring activation functions, dropout rates, epochs, and batch sizes. Empirical distributions of predictive means and variances from the MCDO demonstrate the results of the dual-output MCDO DNNs. The proposed method achieves a significant improvement in forecast accuracy, with an RMSE reduction of about 10% compared to the seasonal autoregressive integrated moving average model. Additionally, the method provides more reliable forecast intervals, as evidenced by a higher coverage proportion and narrower interval widths. A case study on Thailand’s durian export data showcases the method’s utility and applicability to other datasets with trends and/or seasonal components. |
| format | Article |
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| institution | OA Journals |
| issn | 2571-9394 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
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| series | Forecasting |
| spelling | doaj-art-9be8a7b56596485f91928e854fad4c3b2025-08-20T01:55:27ZengMDPI AGForecasting2571-93942024-08-016361663610.3390/forecast6030033Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian ExportsUnyamanee Kummaraka0Patchanok Srisuradetchai1Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Pathum Thani 12120, ThailandDeep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals, especially for time series with trends and seasonal patterns. This paper explicitly details a practical approach integrating dual-output Monte Carlo Dropout (MCDO) with DNNs to approximate predictive means and variances within a Bayesian framework, enabling forecast interval construction. The dual-output architecture employs a custom loss function, combining mean squared error with Softplus-derived predictive variance, ensuring non-negative variance values. Hyperparameter optimization is performed through a grid search exploring activation functions, dropout rates, epochs, and batch sizes. Empirical distributions of predictive means and variances from the MCDO demonstrate the results of the dual-output MCDO DNNs. The proposed method achieves a significant improvement in forecast accuracy, with an RMSE reduction of about 10% compared to the seasonal autoregressive integrated moving average model. Additionally, the method provides more reliable forecast intervals, as evidenced by a higher coverage proportion and narrower interval widths. A case study on Thailand’s durian export data showcases the method’s utility and applicability to other datasets with trends and/or seasonal components.https://www.mdpi.com/2571-9394/6/3/33dual-output neural networkMonte Carlo Dropoutforecast intervaltime-series forecastingregularization |
| spellingShingle | Unyamanee Kummaraka Patchanok Srisuradetchai Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports Forecasting dual-output neural network Monte Carlo Dropout forecast interval time-series forecasting regularization |
| title | Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports |
| title_full | Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports |
| title_fullStr | Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports |
| title_full_unstemmed | Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports |
| title_short | Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports |
| title_sort | time series interval forecasting with dual output monte carlo dropout a case study on durian exports |
| topic | dual-output neural network Monte Carlo Dropout forecast interval time-series forecasting regularization |
| url | https://www.mdpi.com/2571-9394/6/3/33 |
| work_keys_str_mv | AT unyamaneekummaraka timeseriesintervalforecastingwithdualoutputmontecarlodropoutacasestudyondurianexports AT patchanoksrisuradetchai timeseriesintervalforecastingwithdualoutputmontecarlodropoutacasestudyondurianexports |