Zobrazeno 1 - 10
of 255
pro vyhledávání: '"BERGMEIR, CHRISTOPH"'
Autor:
Lewington, Aiden, Vittalam, Alekhya, Singh, Anshumaan, Uppuluri, Anuja, Ashok, Arjun, Athmaram, Ashrith Mandayam, Milt, Austin, Smith, Benjamin, Weinberger, Charlie, Sarin, Chatanya, Bergmeir, Christoph, Chang, Cliff, Patel, Daivik, Li, Daniel, Bell, David, Cao, Defu, Shin, Donghwa, Kang, Edward, Zhang, Edwin, Li, Enhui, Chen, Felix, Smithline, Gabe, Chen, Haipeng, Gasztowtt, Henry, Shin, Hoon, Zhang, Jiayun, Gray, Joshua, Low, Khai Hern, Patel, Kishan, Cooke, Lauren Hannah, Burstein, Marco, Kalapatapu, Maya, Mittal, Mitali, Chen, Raymond, Zhao, Rosie, Majid, Sameen, Potlapalli, Samya, Wang, Shang, Patel, Shrenik, Li, Shuheng, Komaragiri, Siva, Lu, Song, Siangjaeo, Sorawit, Jung, Sunghoo, Zhang, Tianyu, Mao, Valery, Krishnakumar, Vikram, Zhu, Vincent, Kam, Wesley, Li, Xingzhe, Liu, Yumeng
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible A
Externí odkaz:
http://arxiv.org/abs/2412.06936
Autor:
Zhou, Xin, Wang, Weiqing, Buntine, Wray, Qu, Shilin, Sriramulu, Abishek, Tan, Weicong, Bergmeir, Christoph
Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-independent models cannot capture. However, the number of channels in real-w
Externí odkaz:
http://arxiv.org/abs/2408.04245
Autor:
Genov, Evgenii, Ruddick, Julian, Bergmeir, Christoph, Vafaeipour, Majid, Coosemans, Thierry, Garcia, Salvador, Messagie, Maarten
This research addresses the challenge of integrating forecasting and optimization in energy management systems, focusing on the impacts of switching costs, forecast accuracy, and stability. It proposes a novel framework for analyzing online optimizat
Externí odkaz:
http://arxiv.org/abs/2407.03368
In Smyl et al. [Local and global trend Bayesian exponential smoothing models. International Journal of Forecasting, 2024.], a generalised exponential smoothing model was proposed that is able to capture strong trends and volatility in time series. Th
Externí odkaz:
http://arxiv.org/abs/2407.00492
Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hiera
Externí odkaz:
http://arxiv.org/abs/2405.18693
Real-world time series often exhibit complex interdependencies that cannot be captured in isolation. Global models that model past data from multiple related time series globally while producing series-specific forecasts locally are now common. Howev
Externí odkaz:
http://arxiv.org/abs/2405.07117
Publikováno v:
Information Sciences, 625, 700-714 (2023)
Graph Neural Networks (GNN) have recently gained popularity in the forecasting domain due to their ability to model complex spatial and temporal patterns in tasks such as traffic forecasting and region-based demand forecasting. Most of these methods
Externí odkaz:
http://arxiv.org/abs/2312.03903
Publikováno v:
In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE
Psychometric assessment instruments aid clinicians by providing methods of assessing the future risk of adverse events such as aggression. Existing machine learning approaches have treated this as a classification problem, predicting the probability
Externí odkaz:
http://arxiv.org/abs/2312.01029
Autor:
Pekaslan, Direnc, Alonso-Moral, Jose Maria, Bandara, Kasun, Bergmeir, Christoph, Bernabe-Moreno, Juan, Eigenmann, Robert, Einecke, Nils, Ergen, Selvi, Godahewa, Rakshitha, Hewamalage, Hansika, Lago, Jesus, Limmer, Steffen, Rebhan, Sven, Rabinovich, Boris, Rajapasksha, Dilini, Song, Heda, Wagner, Christian, Wu, Wenlong, Magdalena, Luis, Triguero, Isaac
This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Tec
Externí odkaz:
http://arxiv.org/abs/2311.04007
Autor:
Long, Xueying, Bui, Quang, Oktavian, Grady, Schmidt, Daniel F., Bergmeir, Christoph, Godahewa, Rakshitha, Lee, Seong Per, Zhao, Kaifeng, Condylis, Paul
The recent M5 competition has advanced the state-of-the-art in retail forecasting. However, we notice important differences between the competition challenge and the challenges we face in a large e-commerce company. The datasets in our scenario are l
Externí odkaz:
http://arxiv.org/abs/2311.00993