Enhancing time series prediction with Hybrid AFSA-TCN: A unified approach to temporal data and optimization
Time series data, with its sequential dependencies presents a unique challenge for traditional machine learning methods such as Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT), which often struggle to capture temporal patterns effectively. In contrast, Temporal Convolution...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Arqii Publication
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/47223/1/Enhancing%20time%20series%20prediction%20with%20Hybrid%20AFSA-TCN.pdf https://umpir.ump.edu.my/id/eprint/47223/ http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/868 |
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