Optimizing Risk-based Stock Return Prediction in Tehran Stock Exchange industries: A Data Envelopment Analysis by Hamidreza Akhbari, Heydar Mohammadzadeh Salteh, Rasoul Baradaran Hassanzadeh, Mehdi Zeynali

“…In the second stage, using Data Envelopment Analysis (DEA), stock return forecast optimization by the previous stage was optimized for selected industries of the Tehran Stock Exchange from 2010 to 2019. …”
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Trend analysis and prediction of disease burden of occupational carbon monoxide poisoning in China based on the past two decades by Yao Xiao, Chaocheng Wang, Yu Du, Qin Zhang, LinShen Xie

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Runoff Forecasting Research Coupling Quadratic Factor Screening and Deep Learning by CHENG Liwen, HUANG Shengzhi, LI Pei, LI Ziyan, JIA Songtao, HUANG Qiang

“…The effective screening of factors influencing runoff is a key aspect of runoff forecasting research.However,there are many factors affecting runoff,and these factors have complex interactions.Most of the existing studies use numerically driven models with primary factor screening,and the results show that the input factors are spatially redundant,leading to poor forecasting results.In view of this,the support vector regression (SVR) and the long-short memory network model (LSTM) are compared with Weihe River Basin as an example,and the LSTM model is selected as the optimal forecasting model.Principal component analysis and gray correlation analysis are used for secondary screening of the input terms to form a model coupling principal component analysis,gray correlation analysis,and LSTM.The results show that:①the fitting accuracy of LSTM is higher than that of SVR;②the secondary screening of the input terms improves the forecast accuracy,and the forecast accuracy of the coupled model is better than that of the single model,specifically,the model accuracy evaluation indexes of the coupled model are substantially improved compared with those of the single model;③the Nash efficiency coefficient and deterministic coefficient of the coupled model of gray system correlation analysis are improved by 0.13% and 0.03%,respectively,compared with those of the coupled model of principal component analysis,and the standard deviation ratio of observed values is improved by 42.9%.The study shows that the secondary factor screening by using gray correlation can effectively improve forecast accuracy.…”
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FORECASTING FINANCIAL TIME SERIES USING A METHOD OF SELFORGANIZED CRITICALITY by Michail E. Mazurov

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EFFECTIVENESS AND SIGNIFICANCE OF CROP YIELD FORECAST MONITORING CONSTITUTED ON VEGETATION INDICES by Bozhidar IVANOV, Veselin KRUSTEV, Bojin BOJINOV

“…The article presents the post-hoc analysis on the crop yield forecasting on three crops across EU carried out by JRS-Mars: wheat, maize, sunflower. …”
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Global, regional, and national burden of uterine cancer among women aged 50 years and older from 1990 to 2021: a systematic analysis for the global burden of disease study 2021 by Tingting Feng, Wei Li, Qin Wang, Jing Yang, Fujin Shen

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Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

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Forecasting the Effects of the New SARS-CoV-2 Variant in Europe by Iván Area, Juan J. Nieto

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Patterns and Variation in the Weather Forecast: Can Prosodic Features be Predicted Too? by Susan Moore Mauroux

“…The aim of this article is to explore a specific type of media programme: the weather forecast. The analysis is based on a corpus of weather forecasts for the UK, mainly from the Met Office, with five different speakers. …”
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Optimizing OLAP Cube for Supporting Business Intelligence and Forecasting in Banking Sector by Sonali Mathur, Shankar Lal Gupta, Payal Pahwa

“…The data stored in data warehouse is used for making strategic decisions by integrating heterogeneous data from multiple sources at a single storage place, where data is used for querying and analysis purposes. With the advancement in the technology, Business Analytics and Business intelligence are being increasingly used in the financial sector for forecasting business decisions. …”
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Assessment of Socio-Economic Development Forecast for the Russian Federation in 2019–2024 by artiсle Editorial

“…Special attention has been paid to the analysis of forecasted rates and projected sources of economic growth and prospects for social development. …”
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Smart forecasting: Enhancing virtual power plant performance with analytical frameworks by Maja Celeska Krstevska, Petar Krstevski, Mare Srbinovska, Vesna Andova, Aleksandra Krkoleva Mateska

“…This paper presents a comprehensive framework for efficient electricity forecasting within a Virtual Power Plant (VPP) context. …”
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Forecasting of Socio-Economic Development of Regions in Kazakhstan: Principles, Features and Disadvantages by L. A. Balgarina, S. A. Jumabayev, Y. K. Shokamanov

“…The main sources of information of the study were the observation of the procedure for making a forecast of the region and the analysis of legal acts regulating the forecasting processes in the Republic of Kazakhstan. …”
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COMPARISON OF B-SPLINE AND TRUNCATED SPLINE REGRESSION MODELS FOR TEMPERATURE FORECAST by Sri Sulistijowati Handajani, Hasih Pratiwi, Respatiwulan Respatiwulan, Niswatul Qona’ah, Monica Ramadhania, Niken Evitasi, Nindya Eka Apsari

“…In this study, the results of the analysis of the B-Spline regression model and the Spline Truncated model were compared on temperature data at several stations on Java Island to obtain the best model that can be used to forecast the temperature for the next few days. …”
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Forecasting the possibility of developing low-carbon energy sources in the Chelyabinsk Region by Dvinin Dmitry, Davankov Aleksey, Malygin Nikita, Sidelnikov Pavel

“…The article forecasts the possibility of developing various low-carbon energy sources in the Chelyabinsk Region. …”
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Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting by Sarah J. Mohammed, Salah L. Zubaidi, Nadhir Al-Ansari, Hussein Mohammed Ridha, Anmar Dulaimi, Ruqayah Al-Khafaji

“…With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. …”
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Forecasting the formation of freshets in small lowland catchment for different scenarios of urbanization by Katarzyna Krężałek

“…Different variants of forecasts were developed of the impact of introducing various forms of urbanization on the formation of river runoff during freshets in the small, lowland catchment of upper Mławka river, covering the area of 66 km2. …”
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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

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Future of Agrivoltaic projects: A review from the technological forecasting perspective by Leticia Pekk, Petar Sabev Varbanov, Ting Pan, Zoltan Weltsch, Bernadett Radli-Burjan, Andras Hary, Xue-Chao Wang

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The Accuracy of the Equity's Forecast in the Option Model Simulated by Real Volatility Distribution by Zbigniew Krysiak

“…The article is devoted to the assessment of the accuracy of the binomial model in forecasting the value of capital using the Back-Test approach. …”
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