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Academic Articles & Research Papers
Showing 19 of 39 academic articles
James W. Mickens, Brian D. Noble (2006)
Loosely-coupled distributed systems have significant scale and cost advantages over more traditional architectures, but the availability of the nodes in these systems varies widely. Availability modeling is crucial for predicting per-machin...
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R. Glen Donaldson, Mark J. Kamstra • Journal of Forecasting
(1996)
This paper investigates the use of Artificial Neural Networks (ANNs) to combine time series forecasts of stock market volatility from the USA, Canada, Japan and the UK. We demonstrate that combining with nonlinear ANNs generally produces fo...
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DOI: https://doi.org/10.1002/(sici)1099-131x(199601)15:1<49::aid-for604>3.0.co;2-2
Tim Hill, Marcus J. O’Connor, William Remus • Management Science
(1996)
Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series ...
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DOI: https://doi.org/10.1287/mnsc.42.7.1082
Stephan Rasp, Sebastian Lerch • Monthly Weather Review
(2018)
Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the pa...
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other-oa
DOI: https://doi.org/10.1175/mwr-d-18-0187.1
Omran Gheisar, Sima Eskandari Sabzi, ali salmanpour, Seyed Yosef Hajiasghari • زن در توسعه و سیاست
(2024)
The trend of population growth in the last three decades will cause extensive changes in the age structure of Iran's population. So that it can be one of the most important challenges of the country in the coming decades. This development w...
DOAJ
Open Access (DOAJ)
DOI: 10.22059/jwdp.2023.362558.1008358
Chung-Chieh Wang, Wei-Kuo Soong, Chih-Wei Chien, Chih-Sheng Chang et al. • Atmosphere
(2023)
Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon q...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/atmos14061047
Yao Zhang, Fan Lin, Ke Wang • Energies
(2020)
The accuracy of wind power forecasting depends a great deal on the data quality, which is so susceptible to cybersecurity attacks. In this paper, we study the cybersecurity issue of short-term wind power forecasting. We present one class of...
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Open Access (DOAJ)
DOI: 10.3390/en13153780
S. Lou, S. Lou, S. Lou, M. Shrivastava et al. • Atmospheric Chemistry and Physics
(2025)
We used the CAM5 model to examine how different particle-bound polycyclic aromatic hydrocarbon (PAH) degradation approaches affect the spatial distribution of benzo(a)pyrene (BaP). Three approaches were evaluated: NOA (no effect of OA coati...
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Open Access (DOAJ)
DOI: 10.5194/acp-25-8163-2025
Arif TUNCAL, Suat USLU, Erdal DURSUN • Revista de Investigaciones Universidad del Quindío
(2021)
Covid-19, which was defined as a result of research conducted in a group of patients developing respiratory symptoms in late December 2019 in Wuhan province of China, spread to other countries in a very short time by infecting from people t...
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Open Access (DOAJ)
DOI: 10.33975/riuq.vol33n2.603
Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar, Yacine Belhocine • Acta Informatica Pragensia
(2024)
Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system fo...
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Open Access (DOAJ)
DOI: 10.18267/j.aip.246
Noor Hafizah Khairul Anuar, Mohd Amri Md Yunus, Muhammad Ariff Baharudin, Sallehuddin Ibrahim et al. • Journal of Computing Research and Innovation
(2024)
Environmental factors like temperature, solar irradiance, and rain may influence the health and productivity of stingless bees. This paper aims to investigate the best approaches applied in meliponiculture to predict beehive health and prod...
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Open Access (DOAJ)
Charles Nicholson, Lex Beattie, Matthew Beattie, Talayeh Razzaghi et al. • PLoS ONE
(2022)
COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0267558
Tanattrin Bunnag • Forecasting
(2026)
Geopolitical uncertainty may affect financial markets, but its incremental value for forecasting emerging-market stock returns remains unclear. Using monthly data from January 1990 to July 2026, this study compares ARIMA-GARCH and ARIMAX-GA...
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Open Access (DOAJ)
DOI: 10.3390/forecast8050087
Shihao Gu, Bryan Kelly, Dacheng Xiu • National Bureau of Economic Research
(2018)
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia.We demonstrate large economic gains to investors using machine learning forecasts, in some cases...
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Open Access
DOI: https://doi.org/10.3386/w25398
Juliana Aparecida Anochi, Vinícius Albuquerque de Almeida, Haroldo Fraga de Campos Velho • Remote Sensing
(2021)
Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learni...
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Open Access (DOAJ)
DOI: 10.3390/rs13132468
Shihao Gu, Bryan Kelly, Dacheng Xiu • Review of Financial Studies
(2020)
Abstract We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, i...
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cc-by-nc-nd
DOI: https://doi.org/10.1093/rfs/hhaa009
Zella King, Joseph Farrington, Martin Utley, Enoch Kung et al. • npj Digital Medicine
(2022)
Abstract Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabi...
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Open Access (DOAJ)
DOI: 10.1038/s41746-022-00649-y
Zhixin Lu, Brian R. Hunt, Edward Ott • Chaos An Interdisciplinary Journal of Nonlinear Science
(2018)
A machine-learning approach called "reservoir computing" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes...
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Open Access
DOI: https://doi.org/10.1063/1.5039508
Musa Gün, Ahmet Akusta, Haydar Karadağ • İstanbul İktisat Dergisi
(2025)
This study aims to examine the impact of macro-financial indicators on the housing sales volume in Türkiye and evaluate the forecasting performance of traditional time-series models versus modern machine-learning algorithms. Using monthly d...
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Open Access (DOAJ)
DOI: 10.26650/ISTJECON2025-1707729