Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model by Amir Miraki, Pekka Parviainen, Reza Arghandeh

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Adapting Ensemble‐Calibration Techniques to Probabilistic Solar‐Wind Forecasting by N. O. Edward‐Inatimi, M. J. Owens, L. Barnard, H. Turner, M. Marsh, S. Gonzi, M. Lang, P. Riley

“…Ensemble forecasting generates sets of outputs to create probabilistic forecasts which quantify forecast uncertainty, vital for reliable/actionable forecasts. …”
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Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market by Yuki Osone, Daisuke Kodaira

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Decomposition-Aware Framework for Probabilistic and Flexible Time Series Forecasting in Aerospace Electronic Systems by Yuanhong Mao, Xin Hu, Yulang Xu, Yilin Zhang, Yunan Li, Zixiang Lu, Qiguang Miao

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Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting by Zhuofu Deng, Binbin Wang, Heng Guo, Chengwei Chai, Yanze Wang, Zhiliang Zhu

“…Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles. …”
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Probabilistic Forecasting of Crude Oil Prices Using Conditional Generative Adversarial Network Model with Lévy Process by Mohammed Alruqimi, Luca Di Persio

“…By employing conditional supervised training, the inherent structure of the data distribution is preserved, thereby enabling more accurate and reliable probabilistic price forecasts. Additionally, the CO-CGAN integrates a Lévy process and sentiment features to better account for uncertainties and price shocks in the crude oil market. …”
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Probabilistic forecasting of multiple plant day-ahead renewable power generation sequences with data privacy preserving by Hong Liu, Zijun Zhang

Subjects: “…Probabilistic forecast…”
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A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques by Mamunur Rashid, Tarek Elfouly, Nan Chen

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Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation by Aamir Ali, Sumbal Aslam, Sohrab Mirsaeidi, Noor Hussain Mugheri, Riaz Hussain Memon, Ghulam Abbas, Hammad Alnuman

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Forecast Customization System (FOCUS): A Multimodel Ensemble-Based Seasonal Climate Forecasting Tool for the Homogeneous Climate Zones of Myanmar by Itesh Dash, Masahiko Nagai, Indrajit Pal

“…The predictability of raw GCMs, bias-corrected GCMs, and the MMEs was evaluated using RMSE, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the relative operating characteristics (ROC) scores, to quantify the uncertainty in the GCMs. …”
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Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach by Xiaoqing Dai, Lijun Sun, Yanyan Xu

“…The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. …”
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FORECASTING PRACTICES IN ACADEMIC IR: METHODOLOGICAL MAINSTREAM AND UNSOLVED PROBLEMS by I. V. Fomin, K. P. Kokarev, B. I. Ananyev, N. Yu. Silaev, A. A. Sushentsov, A. D. Chekov

“…It showed that academic forecasting in international relations is dominated by quantitative methods and positivist non-paradigmatic approaches. …”
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Estimating the Prevalence of Opioid use Disorder in the Cincinnati Region using Probabilistic Multiplier Methods and Model Averaging by Peter J. Mallow, Nila Sathe, Michael Topmiller, Jennifer Chubinski, Dillon Carr, Roni Christopher

“…**Methods:** Three individual probabilistic simulation models were developed to estimate the number of OUD individuals in the Cincinnati Core Based Statistical Area (CBSA). …”
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FORESIGHT IN FUTURE STUDIES OF RUSSIAN EDUCATION by Irina R. Nazarova

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On predictive modeling of the twitter-based sales data using a new probabilistic model and machine learning methods by Min Wan, Mohammed A. Alshahrani, Najla M. Aloraini, Alia A. Alkhathami, Haifa Alqahtani

“…Furthermore, we employ two machine learning techniques to forecast the sales, with a particular emphasis on 1-step and 3-step advance predictions. …”
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Forecasting insect abundance using time series embedding and machine learning by Gabriel R. Palma, Rodrigo F. Mello, Wesley A.C. Godoy, Eduardo Engel, Douglas Lau, Charles Markham, Rafael A. Moral

“…A possible solution to enhance decision-making is to apply forecasting methods to predict insect abundance. However, another layer of complexity is added when other covariates are considered in the forecasting, such as climate time series collected along the monitoring system. …”
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Empirical prediction intervals applied to short term mortality forecasts and excess deaths by Ricarda Duerst, Jonas Schöley

“…This research addresses this issue by analyzing the error distribution in forecasts of weekly deaths. By deriving empirical prediction intervals, we provide a more accurate probabilistic study of weekly expected and excess deaths compared to the use of conventional parametric intervals. …”
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The development of mathematical model of process of information system monitoring by N. R. Storozhenko, A. I. Goleva

“…With the help of the Markov chains there is developed a mathematical model that considers the dependence of probability of events on time and describes the process of monitoring the faulty parameters of network equipment. The method of probabilistic forecasting of the state of total network failure is proposed. …”
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Forecasting stroke and stroke-driven dementia in a rapidly ageing population: a model-based analysis of alternative projection scenarios for Ireland by Anne Hickey, Eithne Sexton, Frances Horgan, Kathleen E Bennett, Ronan Collins, Martin O'Flaherty, Suzanne Timmons, Chris Macey, David J. Williams

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Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0 by Z. Engberg, R. Teoh, T. Abbott, T. Dean, M. E. J. Stettler, M. L. Shapiro

“…Here, we develop a contrail forecasting tool that produces global maps of persistent contrail formation and their EF<span class="inline-formula">_contrail</span> formatted to align with standard weather and turbulence forecasts for integration into existing flight planning and air traffic management workflows. …”
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