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Zitao Du, Wenbo Yang, Yuna Yin, Xinwei Ma and Jiacheng Gong
When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration...
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Francisca Lanai Ribeiro Torres, Luana Medeiros Marangon Lima, Michelle Simões Reboita, Anderson Rodrigo de Queiroz and José Wanderley Marangon Lima
Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite t...
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Rejath Jose, Faiz Syed, Anvin Thomas and Milan Toma
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library?a Python-based machine learning toolkit?to construct and refine predictive models for...
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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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Shamotra Oad, Monzur Alam Imteaz and Fatemeh Mekanik
Water resources systems planning, and control are significantly influenced by streamflow forecasting. The streamflow in northern and north-central regions of Victoria (Australia) is influenced by different climate indices, such as El Niño Southern Oscill...
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Ashish Sedai, Rabin Dhakal, Shishir Gautam, Anibesh Dhamala, Argenis Bilbao, Qin Wang, Adam Wigington and Suhas Pol
The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-te...
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Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo and Francesco Camastra
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep lea...
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Amirhossein Sohrabbeig, Omid Ardakanian and Petr Musilek
Over the past few years, there has been growing attention to the Long-Term Time Series Forecasting task and solving its inherent challenges like the non-stationarity of the underlying distribution. Notably, most successful models in this area use decompo...
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Natalí Carbo-Bustinza, Hasnain Iftikhar, Marisol Belmonte, Rita Jaqueline Cabello-Torres, Alex Rubén Huamán De La Cruz and Javier Linkolk López-Gonzales
In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these ...
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Qingliang Xiong, Mingping Liu, Yuqin Li, Chaodan Zheng and Suhui Deng
Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilitie...
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