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of 66
pro vyhledávání: '"A. Hewamalage"'
Autor:
S. Krishnapradeep, R. Ekanayake, N. Weerasooriya, A. Hewamalage, P. Wickramasinghe, R. M. Mudiyanse
Publikováno v:
Sri Lanka Journal of Medicine, Vol 33, Iss 2, Pp 25-32 (2024)
Introduction: Promoting healthy parenting practices is considered to be a part of public health midwives’ (PHMs) job in Sri Lanka. However, they do not undergo specific training to impart parenting skills to parents. Objectives: Objectives of this
Externí odkaz:
https://doaj.org/article/8497bf576636468cb3ef9609513bdf9e
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
Machine Learning (ML) and Deep Learning (DL) methods are increasingly replacing traditional methods in many domains involved with important decision making activities. DL techniques tailor-made for specific tasks such as image recognition, signal pro
Externí odkaz:
http://arxiv.org/abs/2203.10716
Autor:
Triebe, Oskar, Hewamalage, Hansika, Pilyugina, Polina, Laptev, Nikolay, Bergmeir, Christoph, Rajagopal, Ram
We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferation of time series data, explainable forecasting remains a challenging
Externí odkaz:
http://arxiv.org/abs/2111.15397
Forecast evaluation plays a key role in how empirical evidence shapes the development of the discipline. Domain experts are interested in error measures relevant for their decision making needs. Such measures may produce unreliable results. Although
Externí odkaz:
http://arxiv.org/abs/2108.03588
In the current context of Big Data, the nature of many forecasting problems has changed from predicting isolated time series to predicting many time series from similar sources. This has opened up the opportunity to develop competitive global forecas
Externí odkaz:
http://arxiv.org/abs/2012.12485
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univaria
Externí odkaz:
http://arxiv.org/abs/2008.02663
Akademický článek
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Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these context
Externí odkaz:
http://arxiv.org/abs/1909.04293
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as ETS and ARIMA gain their popularity not only from t
Externí odkaz:
http://arxiv.org/abs/1909.00590