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Yajun Wang, Jianping Zhu and Renke Kang
Seasonal?trend-decomposed transformer has empowered long-term time series forecasting via capturing global temporal dependencies (e.g., period-based dependencies) in disentangled temporal patterns. However, existing methods design various auto-correlatio...
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Binhang Wei, Baohui Li, Jiechen Zhao, Yu Liu, Zhijun Wei and Anliang Wang
Liaodong Bay is one of the lowest latitude areas with seasonal sea ice cover in the Northern Hemisphere. Sea ice forecasting faces challenges in accuracy due to its low thickness. Therefore, a novel parameterization scheme for oceanic heat flux was devel...
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Enna Hirata and Takuma Matsuda
With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been wid...
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Yuruixian Zhang, Wei Chong Choo, Jen Sim Ho and Cheong Kin Wan
Tourism forecasting has garnered considerable interest. However, integrating tourism forecasting with volatility is significantly less typical. This study investigates the performance of both the single models and their combinations for forecasting the v...
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Ayesha Ubaid, Farookh Hussain and Muhammad Saqib
Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and m...
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