Research on performance forecasting bias in start-up companies by Yukiko Konno

“…The results of the analysis showed that company size, profitability and optimism of past performance forecasts had a positive impact on performance forecasting bias. …”
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FORECASTING THE DEVELOPMENT OF AGRICULTURAL PRODUCTION IN THE CONTEXT OF FOOD SECURITY by Stepan Petrovich BURLANKOV, Mikhail Аleksandrovich ANANIEV, Alexandr Alexandrovich GAZHUR, Nadezda Vasilievna SEDOVA, Olga Mikhailovna ANANIEVA

“…Defining strategic long-term forecasts of the national food security system development and parameters of the food market based on them is an important area of scientific studies based on a comprehensive analysis of the agro-food market's activity, identifying the existing problems and developing offers on improving management instruments. …”
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Forecasting water discharges on the mountain river Arpa (Armenia) by Gaidukova Ekaterina, Margarian Varduhi, Mirzoev Vagif, Batmazova Anna, Vinokurov Igor

“…The work is aimed at testing mathematical models in the form of differential equations for short-term forecasting of water content of a small mountain river in Armenia. …”
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Interpretable AI for Short-Term Water Demand Forecasting by Aly-Joy Ulusoy, Carlos Jara-Arriagada, Yuanyang Liu, Bradley Jenks, Ivan Stoianov

“…Machine learning models such as artificial neural networks (ANNs) are becoming increasingly popular in short-term water demand forecasting. This is because, despite their lack of interpretability, ANNs are able to capture complex interactions between explanatory variables and water consumption better than a traditional time series analysis or simple linear regression. …”
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Forecasting the Indian Ocean Dipole With Deep Learning Techniques by Jun Liu, Youmin Tang, Yanling Wu, Tang Li, Qiang Wang, Dake Chen

“…The results indicate that the deep learning approach is capable of forecasting the IOD at lead times up to 7 months. The forecast skills of CNN are superior to those of the dynamic models in the North American Multi‐Model Ensemble (NMME). …”
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ANALYSING AND FORECASTING COMPETITIVENESS: THE CASE OF THE TURKISH COTTON INDUSTRY by Deniz SARICA, Merve Muruvvet DAG

“…In 2020, the RSCA index was -0.03. Furthermore, the forecasting analysis shows that the RSCA indices for cotton export will gradually decline due to periodic fluctuations, eventually falling to -0.18 by 2030. …”
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Information and Communication Workforce Forecasting: Evidence from England by Fethi Aslan

“…This result is highly important as it provides a basis of a scenario analysis for different stakeholders on how to plan regarding job loss risks, wages, and education-related matters.…”
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Forecasting seasonal mean temperature over Rangpur, Bangladesh by Zakaria Hossain

“…Statistical and mathematical methods were applied by CPT in this research which included canonical correlation analysis, covariance matrix, and eigenvalues equations.The study found that the forecasted seasonal mean temperature was higher in rainy and winter seasons than the temperature observed and was lower in summer, autumn, late autumn, and spring season than the observed temperature at Rangpur. …”
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Forecasting of Global Market Prices of Major Financial Instruments by Roshani W. Divisekara, Ruwan D. Nawarathna, Lakshika S. Nawarathna

“…The forecasting models were tested with two sample sizes, namely, 5-year close price values for correlation analysis and 3-year close price values for model building from 2013 January to 2018 January. …”
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Methodological approaches to morbidity forecasting in military educational organizations by A. E. Zobov, A. A. Kuzin, R. G. Makiev, A. A. Zobova

“…The difficulty of using the factor approach is noted due to the stochasticity of the epidemic process.Based on the results of a retrospective epidemiological analysis of the personalized morbidity of cadets of the Military Medical Academy, the heterogeneity of military contingents in susceptibility to acute respiratory infections of the upper respiratory tract is shown.From the standpoint of the academician V.D .Belyakov’s et al. theory of the parasitic systems self-regulation, the conclusion is made about the expediency of using a factor approach for epidemiological forecasting of morbidity in organized collectives. …”
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The Impacts of Magnetogram Projection Effects on Solar Flare Forecasting by Griffin T. Goodwin, Viacheslav M. Sadykov, Petrus C. Martens

“…This work explores the impacts of magnetogram projection effects on machine-learning-based solar flare forecasting models. Utilizing a methodology proposed by D. …”
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THE PRINCIPLES AND POSSIBILITIES OF FORECASTING: THE PAYABLES AND RECEIVABLES BALANCE SETTLEMENTS by T. P. Karpova, V. V. Karpova

“…The method includes five stages of constructing the balance of payments: analysis and preparation of initial data for predicting receivables and payables, systematization and classification of the receivables and the payables, construction of parametric trend model for regular and irregular payables and receivables and for financial and non-financial responsibility, preparation of the aggregate forecast of movements of accounts payable and accounts receivable. …”
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Strategic forecasting of complex production system - railway transport by E. A. Sotnikov, K. P. Shenfeld

“…The paper considers conditions and methods for developing a strategic forecast for the technical and economic state of the worldwide and Russian railway transport for a period of 30-50 years or more. …”
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Meteorological drought severity forecasting utilizing blended modelling by Aaditya Ahire, Nilima Zade, Umeed Mujawar, Dimple Mehta, Ketan Kotecha

“…The research proposes an ensemble of Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), and Tabular Network (TabNet) for a higher accuracy in drought forecasting. With the large meteorological dataset that involves temperature, precipitation, humidity, and wind speed as features, the model integrates: • The tree capabilities of XGBoost perform feature selection very effectively. • Temporal Pattern Analysis using LSTM. • Insight obtained from the attention mechanism-based TabNet.Empirical results demonstrate that the proposed ensemble outperforms individual models, achieving the lowest Root Mean Square Error (RMSE: 0.6582) and Mean Absolute Error (MAE: 0.5377), and the highest Coefficient of Determination (R²: 0.5069). …”
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Improving Deep Learning for Forecasting Accuracy in Financial Data by Shih-Lin Lin, Hua-Wei Huang

“…LSTM is a good way to forecast cyclic or seasonal data. The forecast result is obtained by adding all the IMFs together. …”
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Model of adaptive information system for forecasting crop productivity by С. І. Мельник, О. І. Присяжнюк, Є. М. Стариченко, К. М. Мажуга, В. В. Бровкін, О. М. Мартинов, В. В. Маслечкін

“…Results. A detailed analysis of conceptual approaches to the construction of mathematical agricultural models is carried out and the main advantages and disadvantages of modern analogues are established. …”
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Improving the reliability of the forecast of the quality of carbonate raw materials by К. V. Repina

“…Implementation of selective mining of carbonate rock deposits requires preliminary forecasting of karst formations in the strata of primary rocks.Aim. …”
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Postprocessing of convection permitting precipitation forecast using UNets by Marcos Esquivel-González, Albano González, Juan Carlos Pérez, Juan Pedro Díaz

“…Reliable precipitation forecasting is crucial in sectors like public safety, agriculture and water management. …”
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A Precipitation Forecast Score Based on Potential Impact by Liujie PAN, Hongfang ZHANG, Chunjuan QI, Shan LU, Lili DU

“…Impact" is defined as the characterization of potential consequences that may result from forecast hits or misses on actual precipitation occurrences.For hit and missed events， impact factors are defined by taking the logarithm of observed precipitation and the logarithm of the difference between observed and forecasted precipitation.Based on this， equivalent impacts （AI and CI） of hit and missed events are accumulated at the spatiotemporal scale.The scoring solely considering the impact of CI is defined as a sub-item of the Impact Threat Scoring （ITS）.The precipitation scoring that takes into account the combined effects of AI and CI is defined as the ITS score.Analysis shows that ITS0 assigns dynamic weights based on the degree of difference in missed precipitation， allowing for a clear distinction of the impact level of missed events.On the other hand， ITS rewards the accurate prediction of heavy precipitation.The larger the hit precipitation values and the smaller the difference between the forecasted and observed values in missed events， the larger the ITS score.These factors result in a better ability to depict the potential consequences on actual precipitation.…”
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Analysis of association of polymorphic variants of genes GSTP1 and GSTM1 with survival rate sick of liver cirrhosis of a virus and alcoholic etiology by M. I. Rachkovskiy, I. A. Goncharova, E. I. Beloborodova, Ye. V. Beloborodova, Ye. G. Gruzdeva, A. S. Alekseyeva, V. P. Puzyryov

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