DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting
This work presents a novel hybrid framework called DCEnt-PredictiveNet (deep convolutional neural network (DCNN) + entropy + support vector regressor (SVR)) that concatenate both deep and handcrafted features for time series data analysis and forecasting. From the discrete wavelet transform coeffici...
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sg-ntu-dr.10356-1807162024-10-22T00:36:30Z DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting Sudarshan, Vidya K. Ramachandra, Reshma A. Ojha, Smit Tan, Ru-San College of Computing and Data Science Computer and Information Science Time series forecasting COVID prediction This work presents a novel hybrid framework called DCEnt-PredictiveNet (deep convolutional neural network (DCNN) + entropy + support vector regressor (SVR)) that concatenate both deep and handcrafted features for time series data analysis and forecasting. From the discrete wavelet transform coefficients of input time series data, computed four different handcrafted entropy features, which were then concatenated with deep features extracted using a modified DCNN. The concatenated deep and handcrafted feature vector was then fed to a SVR for prediction. The DCEnt-PredictiveNet framework was trained and tested on three time series datasets of real-world COVID-19, stock price and traffic information, and achieved mean absolute percentage errors of 0.03 %, 1.53 % and 11.41 % for daily cumulative COVID-19 positive cases, closing stock price, and hourly traffic (vehicle numbers) at one junction predictions, respectively. In addition, we incorporated local interpretable model-agnostic explanations and Shapley additive explanations methods into DCEnt-PredictiveNet to enable visualization of significant features that contributed to the model's decision-making, thereby enhancing its explainability. Our DCEnt-PredictiveNet model yielded promising and interpretable forecasting results, which can facilitate advance resource planning in hospitals for incoming COVID-19 patients, stock market investment planning, and efficient traffic control management. 2024-10-22T00:36:30Z 2024-10-22T00:36:30Z 2024 Journal Article Sudarshan, V. K., Ramachandra, R. A., Ojha, S. & Tan, R. (2024). DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting. Neurocomputing, 608, 128389-. https://dx.doi.org/10.1016/j.neucom.2024.128389 0925-2312 https://hdl.handle.net/10356/180716 10.1016/j.neucom.2024.128389 2-s2.0-85202200005 608 128389 en Neurocomputing © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. |
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Computer and Information Science Time series forecasting COVID prediction Sudarshan, Vidya K. Ramachandra, Reshma A. Ojha, Smit Tan, Ru-San DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting |
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This work presents a novel hybrid framework called DCEnt-PredictiveNet (deep convolutional neural network (DCNN) + entropy + support vector regressor (SVR)) that concatenate both deep and handcrafted features for time series data analysis and forecasting. From the discrete wavelet transform coefficients of input time series data, computed four different handcrafted entropy features, which were then concatenated with deep features extracted using a modified DCNN. The concatenated deep and handcrafted feature vector was then fed to a SVR for prediction. The DCEnt-PredictiveNet framework was trained and tested on three time series datasets of real-world COVID-19, stock price and traffic information, and achieved mean absolute percentage errors of 0.03 %, 1.53 % and 11.41 % for daily cumulative COVID-19 positive cases, closing stock price, and hourly traffic (vehicle numbers) at one junction predictions, respectively. In addition, we incorporated local interpretable model-agnostic explanations and Shapley additive explanations methods into DCEnt-PredictiveNet to enable visualization of significant features that contributed to the model's decision-making, thereby enhancing its explainability. Our DCEnt-PredictiveNet model yielded promising and interpretable forecasting results, which can facilitate advance resource planning in hospitals for incoming COVID-19 patients, stock market investment planning, and efficient traffic control management. |
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College of Computing and Data Science |
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College of Computing and Data Science Sudarshan, Vidya K. Ramachandra, Reshma A. Ojha, Smit Tan, Ru-San |
format |
Article |
author |
Sudarshan, Vidya K. Ramachandra, Reshma A. Ojha, Smit Tan, Ru-San |
author_sort |
Sudarshan, Vidya K. |
title |
DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting |
title_short |
DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting |
title_full |
DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting |
title_fullStr |
DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting |
title_full_unstemmed |
DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting |
title_sort |
dcent‐predictivenet: a novel explainable hybrid model for time series forecasting |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/180716 |
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1814777814160769024 |