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Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting.However, those transformer-based models suffer a severe…

Machine Learning · Computer Science 2022-06-27 Tian Zhou , Jianqing Zhu , Xue Wang , Ziqing Ma , Qingsong Wen , Liang Sun , Rong Jin

We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements, including Residual DCN, which adds attention…

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron…

Machine Learning · Computer Science 2024-04-05 Xuesong Wang , Nina Fatehi , Caisheng Wang , Masoud H. Nazari

The manpower scheduling problem is a kind of critical combinational optimization problem. Researching solutions to scheduling problems can improve the efficiency of companies, hospitals, and other work units. This paper proposes a new model…

Machine Learning · Computer Science 2021-05-11 Tianyu Liu , Lingyu Zhang

Accurate forecasting of long-term time series has important applications for decision making and planning. However, it remains challenging to capture the long-term dependencies in time series data. To better extract long-term dependencies,…

Machine Learning · Computer Science 2024-05-15 Feifei Li , Suhan Guo , Feng Han , Jian Zhao , Furao Shen

With the volatile and complex nature of financial data influenced by external factors, forecasting the stock market is challenging. Traditional models such as ARIMA and GARCH perform well with linear data but struggle with non-linear…

Machine Learning · Computer Science 2025-01-30 Prashant Pilla , Raji Mekonen

Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and…

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models. Yet, despite the attractive properties of time-index models, such as being able to…

Machine Learning · Computer Science 2023-10-18 Gerald Woo , Chenghao Liu , Doyen Sahoo , Akshat Kumar , Steven Hoi

Accurate electrical load forecasting is crucial for optimizing power system operations, planning, and management. As power systems become increasingly complex, traditional forecasting methods may fail to capture the intricate patterns and…

Systems and Control · Electrical Eng. & Systems 2024-11-26 Elias Raffoul , Mingjian Tuo , Cunzhi Zhao , Tianxia Zhao , Meng Ling , Xingpeng Li

The growing instability of both global and domestic economic environments has increased the risk of financial distress at the household level. However, traditional econometric models often rely on delayed and aggregated data, limiting their…

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality…

Systems and Control · Electrical Eng. & Systems 2024-08-05 Renyou Xie , Xin Yin , Chaojie Li , Guo Chen , Nian Liu , Bo Zhao , Zhaoyang Dong

Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modelling. We propose here an alternate approach based on deep learning algorithms for…

Machine Learning · Computer Science 2021-02-19 Mohita Chaudhary , Mohamed Sadok Gastli , Lobna Nassar , Fakhri Karray

Recently, emotional speech synthesis has achieved remarkable performance. The emotion strength of synthesized speech can be controlled flexibly using a strength descriptor, which is obtained by an emotion attribute ranking function.…

Sound · Computer Science 2021-10-11 Rui Liu , Berrak Sisman , Haizhou Li

Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often…

With an increasing emphasis on driving down the costs of Operations and Maintenance (O&M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain.…

Machine Learning · Computer Science 2022-07-27 Connor Walker , Callum Rothon , Koorosh Aslansefat , Yiannis Papadopoulos , Nina Dethlefs

Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a…

Machine Learning · Computer Science 2024-04-05 Leona Hennig , Tanja Tornede , Marius Lindauer

Time series data is being used everywhere, from sales records to patients' health evolution metrics. The ability to deal with this data has become a necessity, and time series analysis and forecasting are used for the same. Every Machine…

Machine Learning · Computer Science 2022-11-29 Rameshwar Garg , Shriya Barpanda , Girish Rao Salanke N S , Ramya S

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters…

Instrumentation and Methods for Astrophysics · Physics 2024-02-28 Yasser Abduallah , Khalid A. Alobaid , Jason T. L. Wang , Haimin Wang , Vania K. Jordanova , Vasyl Yurchyshyn , Huseyin Cavus , Ju Jing

Multivariate time series forecasting is an important yet challenging problem in machine learning. Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments…

Machine Learning · Computer Science 2019-12-12 Jiezhu Cheng , Kaizhu Huang , Zibin Zheng

In recent years, advancements in deep learning techniques have considerably enhanced the efficiency and accuracy of medical diagnostics. In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of…

Machine Learning · Computer Science 2024-04-08 Suma K , Deepali Koppad , Preethi Kumar , Neha A Kantikar , Surabhi Ramesh
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