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Journal articles on the topic 'State forecasting'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Jumadilova, Shynara, Nurlan Sailaubekov, and Dana Kunanbayeva. "Company’s financial state forecasting: methods and approaches." Investment Management and Financial Innovations 14, no. 3 (2017): 93–101. http://dx.doi.org/10.21511/imfi.14(3).2017.09.

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Planning company’s activity is a complex process, in which foresight is of great importance. The paper presents a method to predict financial state of a company using available financial data. For the prediction of quantitative indicators of the company currently there are different ways to build predictive models, such as simple and multiple regressions, autoregressive model and others. In this paper, to predict financial indicators of the company we use econometric modeling techniques. Tools to check the time series for the seasonality and stationarity are used in constructing the models. To

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2

Frees, Edward W. "Forecasting State-to-State Migration Rates." Journal of Business & Economic Statistics 10, no. 2 (1992): 153. http://dx.doi.org/10.2307/1391674.

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3

Frees, Edward W. "Forecasting State-to-State Migration Rates." Journal of Business & Economic Statistics 10, no. 2 (1992): 153–67. http://dx.doi.org/10.1080/07350015.1992.10509895.

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4

Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from Decembe

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5

Mohammed Ali, Umar. "Effective Role Of Forecasting And Decision Making In Organization Of Borno State." American Journal of Management and Economics Innovations 03, no. 01 (2021): 20–23. http://dx.doi.org/10.37547/tajmei/volume03issue01-05.

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Forecasting is the Act of anticipating in advancement is likely to happen under a given sat to condition is very important not only to every business organisation but to virtually every significant management decision. The framework of this paper is focus on the nature of the forecasting and the role of the forecasting techniques, which include the qualitative and quantitative, the criteria for choosing forecasting technique and the failure of the business forecasting techniques. However, perfect forecasting is usually impossible because there are so many factor in the business environment tha

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6

Fildes, Robert, and Herman Stekler. "The state of macroeconomic forecasting." Journal of Macroeconomics 24, no. 4 (2002): 435–68. http://dx.doi.org/10.1016/s0164-0704(02)00055-1.

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7

Bachmeier, Lance. "The State of Macroeconomic Forecasting." International Journal of Forecasting 20, no. 4 (2004): 737–38. http://dx.doi.org/10.1016/j.ijforecast.2004.01.001.

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8

Myskiv, Galyna, Tetyana Andreykiv, and Viktoriya Rudevska. "Forecasting of the state of the credit market in Ukraine." Investment Management and Financial Innovations 13, no. 4 (2016): 235–41. http://dx.doi.org/10.21511/imfi.13(4-1).2016.10.

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The article highlights the forecasting of development of the credit market in Ukraine on the basis of regression analysis and based on a number of macroeconomic factors. It provides a matrix of coefficients for pair correlations for the calculation of the volume of loans given by banks and non-bank financial institutions, foreign economic agents and inter-economic actors. It gives partial regression models for determining the volume of loans according to the market’s segments. It carries out the forecasting of the credit market and the volumes of loans given by its segments. Keywords: credit m

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9

Murr, Andreas E., and Michael S. Lewis-Beck. "Citizen Forecasting 2020: A State-by-State Experiment." PS: Political Science & Politics 54, no. 1 (2020): 91–95. http://dx.doi.org/10.1017/s1049096520001456.

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10

Klarner, Carl. "Forecasting the 2010 State Legislative Elections." PS: Political Science & Politics 43, no. 04 (2010): 643–48. http://dx.doi.org/10.1017/s1049096510001101.

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This article offers forecasts made on July 22, 2010, for the 2010 state legislative elections. Most work in the election forecasting field has been done on presidential and U.S. House elections. Less has been done for U.S. Senate elections, and almost none for gubernatorial or state legislative elections. This year will see much attention directed at the 43 state legislatures holding elections, because many will have the responsibility for drawing new district lines based on the 2010 census. Furthermore, of those chambers with elections scheduled in 2010, seven currently contain one party with

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11

Kechkhoshvili, Erekle, and Irina Khutsishvili. "For Flood Forecasting Issues." Works of Georgian Technical University, no. 2(532) (June 10, 2024): 265–72. http://dx.doi.org/10.36073/1512-0996-2024-2-265-272.

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. Global climate change has caused sharp increasing of natural calamities, including floods. In the course of recent period, over the entire world, every year there are occurring tens of cases of disastrous floods and waterflows characterized by damages worth of several millions and human losses. The issue of forecasting waterflows and floods, in general, is discussed in the article. There are given basic differentiating features-characteristics existing between spring floods and rain-caused waterflows. The methodology of forecasting related decision-making based on the Statistical Fuzzy Analy

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12

Magnusson, L., E. Källén, and J. Nycander. "Initial state perturbations in ensemble forecasting." Nonlinear Processes in Geophysics 15, no. 5 (2008): 751–59. http://dx.doi.org/10.5194/npg-15-751-2008.

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Abstract. Due to the chaotic nature of atmospheric dynamics, numerical weather prediction systems are sensitive to errors in the initial conditions. To estimate the forecast uncertainty, forecast centres produce ensemble forecasts based on perturbed initial conditions. How to optimally perturb the initial conditions remains an open question and different methods are in use. One is the singular vector (SV) method, adapted by ECMWF, and another is the breeding vector (BV) method (previously used by NCEP). In this study we compare the two methods with a modified version of breeding vectors in a l

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13

Leuthold, Jane H. "A forecasting model for state expenditures." Public Choice 56, no. 1 (1988): 45–55. http://dx.doi.org/10.1007/bf00052069.

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14

Peláez, Rolando F. "A recession-and-state forecasting model." Southern Economic Journal 81, no. 4 (2015): 1025–39. http://dx.doi.org/10.1002/soej.12060.

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15

Manikandan, M., Ashish Pathani, Akhilesh Dwivedi, Muntather Almusawi, Allirani P, and D. Jeevitha. "Solar Energy Forecasting: Perspectives of the State-Of-The-Art." E3S Web of Conferences 540 (2024): 08008. http://dx.doi.org/10.1051/e3sconf/202454008008.

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Solar energy is a promising renewable energy source, but its intermittent and variable nature poses significant challenges for accurate forecasting. Over the recent years, there has been a remarkable surge in research dedicated to improving the precision of solar energy forecasting models. This review article delves into the state-of-the-art in solar energy forecasting. Beginning with an exploration of the hurdles faced in forecasting solar radiation, we proceed to provide an extensive survey of various forecasting models that have been developed to tackle this complex problem. Factors influen

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16

Lewis, John M. "Roots of Ensemble Forecasting." Monthly Weather Review 133, no. 7 (2005): 1865–85. http://dx.doi.org/10.1175/mwr2949.1.

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Abstract The generation of a probabilistic view of dynamical weather prediction is traced back to the early 1950s, to that point in time when deterministic short-range numerical weather prediction (NWP) achieved its earliest success. Eric Eady was the first meteorologist to voice concern over strict determinism—that is, a future determined by the initial state without account for uncertainties in that state. By the end of the decade, Philip Thompson and Edward Lorenz explored the predictability limits of deterministic forecasting and set the stage for an alternate view—a stochastic–dynamic vie

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17

Tian, Zhongda, Gang Wang, and Yi Ren. "Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation." Wind Engineering 44, no. 2 (2019): 152–67. http://dx.doi.org/10.1177/0309524x19849867.

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In order to improve the forecasting accuracy of short-term wind speed, a forecasting method based on autoregressive moving average with echo state network compensation is proposed in this article. First, the linear and nonlinear characteristics of short-term wind speed can be determined by Brock–Dechert–Scheinkman statistics method. Then, autoregressive moving average model is used for modeling and to forecast the linear component of short-term wind speed. The linear component of short-term wind speed sequence is obtained. Artificial bee colony algorithm–optimized echo state network model is u

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18

Zhao, Junbo, Gexiang Zhang, Zhao Yang Dong, and Massimo La Scala. "Robust Forecasting Aided Power System State Estimation Considering State Correlations." IEEE Transactions on Smart Grid 9, no. 4 (2018): 2658–66. http://dx.doi.org/10.1109/tsg.2016.2615473.

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19

Ming-jun, Deng, and Qu Shi-ru. "Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting." Computational Intelligence and Neuroscience 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/875243.

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Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models,

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20

Hnatenko, Valeriy. "Forecasting personal response to economic state crises." Public administration aspects 8, no. 6 (2020): 6–15. http://dx.doi.org/10.15421/1520100.

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The article examines the concepts of "crisis", "crisis state", "economic crisis", the main characteristics of the crisis, considers the possible options for the behavior of the subject in a crisis state, namely constructive and destructive. The course of the 2008 economic crisis, which manifested itself in a massive drop in production, a drop in demand and prices for raw materials, an increase in unemployment, and a spread to the monetary, fiscal, and social areas, has worsened the social welfare and expectations of businesses and ordinary citizens have been analyzed. The crisis affected not o

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21

Stella, Andrea, and James H. Stock. "A State-Dependent Model for Inflation Forecasting." International Finance Discussion Paper 2012, no. 1062 (2012): 1–24. http://dx.doi.org/10.17016/ifdp.2012.1062.

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22

Stella, Andrea, and James H. Stock. "A State-Dependent Model for Inflation Forecasting." International Finance Discussion Paper 2012, no. 1062r (2012): 1–24. http://dx.doi.org/10.17016/ifdp.2012.1062r.

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23

PESHKOVA, ELENA P., OLGA A. USTINCHENKO, and OLEG R. KRAVCHENKO. "FORECASTING FINANCIAL STATE OF AN ECONOMIC ENTITY." International Scientific Journal, no. 2 (2021): 19–28. http://dx.doi.org/10.34286/1995-4638-2021-77-2-19-28.

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24

Guan, Bo, Emmanuel Sirimal Silva, Hossein Hassani, and Saeed Heravi. "Forecasting tourism growth with State-Dependent Models." Annals of Tourism Research 94 (May 2022): 103385. http://dx.doi.org/10.1016/j.annals.2022.103385.

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25

DZEMYDA, Gintautas, Vydunas SALTENIS, and Vytautas TIESIS. "Forecasting Models in the State Education System." Informatics in Education 2, no. 1 (2003): 3–14. http://dx.doi.org/10.15388/infedu.2003.01.

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26

Svetunkov, Ivan, and John E. Boylan. "State-space ARIMA for supply-chain forecasting." International Journal of Production Research 58, no. 3 (2019): 818–27. http://dx.doi.org/10.1080/00207543.2019.1600764.

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27

Willoughby, H. E., E. N. Rappaport, and F. D. Marks. "Hurricane Forecasting: The State of the Art." Natural Hazards Review 8, no. 3 (2007): 45–49. http://dx.doi.org/10.1061/(asce)1527-6988(2007)8:3(45).

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28

Shkurti, William J. "A User's Guide to State Revenue Forecasting." Public Budgeting & Finance 10, no. 1 (1990): 79–94. http://dx.doi.org/10.1111/1540-5850.00853.

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29

Voorhees, William. "Institutional structures utilized in state revenue forecasting." Journal of Public Budgeting, Accounting & Financial Management 14, no. 2 (2002): 175–95. http://dx.doi.org/10.1108/jpbafm-14-02-2002-b002.

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30

Afanaseva, Olga S., Galina F. Egorova, and Elena A. Afanaseva. "Forecasting state diagrams two-component salt systems." Vestnik of Samara State Technical University. Technical Sciences Series 32, no. 1 (2024): 6–17. http://dx.doi.org/10.14498/tech.2024.1.1.

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The article proposes a method for forecasting and approximate calculating the two-component systems characteristics state diagrams. The results for 200 salt systems with a common cation and 100 with a common anion systems statistical analysis of fase diagrams are presented. In this paper, the authors propose to consider two signs of the eutectic points presence in binary systems and a method for approximate calculation the eutectic point temperature and concentration values. The first criterion for the presence or absence of eutectic points in the system is determined using specific, isobaric

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31

Brown Do Coutto Filho, M., and J. C. S. de Souza. "Forecasting-Aided State Estimation—Part I: Panorama." IEEE Transactions on Power Systems 24, no. 4 (2009): 1667–77. http://dx.doi.org/10.1109/tpwrs.2009.2030295.

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32

Brown Do Coutto Filho, M., J. C. S. de Souza, and R. S. Freund. "Forecasting-Aided State Estimation—Part II: Implementation." IEEE Transactions on Power Systems 24, no. 4 (2009): 1678–85. http://dx.doi.org/10.1109/tpwrs.2009.2030297.

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33

Clements, Michael P. "Comments on ‘The state of macroeconomic forecasting’." Journal of Macroeconomics 24, no. 4 (2002): 469–82. http://dx.doi.org/10.1016/s0164-0704(02)00056-3.

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34

Croushore, D. "Comments on ‘The state of macroeconomic forecasting’." Journal of Macroeconomics 24, no. 4 (2002): 483–89. http://dx.doi.org/10.1016/s0164-0704(02)00057-5.

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35

Smith, Ron. "Comments on ‘The state of macroeconomic forecasting’." Journal of Macroeconomics 24, no. 4 (2002): 491–93. http://dx.doi.org/10.1016/s0164-0704(02)00058-7.

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36

West, Kenneth D. "Comments on ‘The state of macroeconomic forecasting’." Journal of Macroeconomics 24, no. 4 (2002): 495–97. http://dx.doi.org/10.1016/s0164-0704(02)00059-9.

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37

Zellner, Arnold. "Comments on ‘The state of macroeconomic forecasting’." Journal of Macroeconomics 24, no. 4 (2002): 499–502. http://dx.doi.org/10.1016/s0164-0704(02)00060-5.

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38

Cecconi, Giovanni. "Step-ahead state forecasting using streamflow observations." Agricultural Water Management 13, no. 2-4 (1988): 169–83. http://dx.doi.org/10.1016/0378-3774(88)90152-7.

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39

OWYANG, MICHAEL T., JEREMY PIGER, and HOWARD J. WALL. "Forecasting National Recessions Using State-Level Data." Journal of Money, Credit and Banking 47, no. 5 (2015): 847–66. http://dx.doi.org/10.1111/jmcb.12228.

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40

Dowi, Sideig A., and Amar Ibrahim Hamza. "Dynamic State Forecasting in Electric Power Networks." Journal of Power and Energy Engineering 02, no. 03 (2014): 1–11. http://dx.doi.org/10.4236/jpee.2014.23001.

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41

Lvovich, Yakov Ye, Andrey P. Preobrazhenskiy, and Tatiana V. Avetisyan. "OPTIMIZATION AND STATE FORECASTING IN TRANSPORTATION SYSTEMS." International Journal of Advanced Studies 12, no. 3 (2022): 109–24. http://dx.doi.org/10.12731/2227-930x-2022-12-3-109-124.

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Optimization of transport work is necessary in case of uncontrollable growth of the described costs. The process of optimizing transport costs at the enterprise begins with an analysis of the current logistics strategy and collection of recommendations for its correction. The following aspects of the transport system of the company are subject to analysis: the method of movement of goods; the choice of vehicle type and its specific model; the selection of the carrier company and other logistics intermediaries; the layout of the company’s storage terminals. An optimized transport system can red

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42

Louzis, Dimitrios P. "Steady‐state modeling and macroeconomic forecasting quality." Journal of Applied Econometrics 34, no. 2 (2018): 285–314. http://dx.doi.org/10.1002/jae.2657.

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43

Mozharovskyi, Volodymyr, and Oleksiy Solomytskyi. "ASSESSMENT AND FORECASTING METHOD OF STATE STABILITY." Baltic Journal of Economic Studies 9, no. 2 (2023): 157–63. http://dx.doi.org/10.30525/2256-0742/2023-9-2-157-163.

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In the article, the authors propose a method for assessing and forecasting the stability of the state. The relevance of the work is due to the complex military and political processes taking place in and around Ukraine, attempts at large-scale interference in internal affairs (including by military means), attempts to destroy Ukrainian statehood and make the country under total control, and the lack of a methodological apparatus that can be used to assess the state's ability to withstand external and internal threats and preserve its own identity and structural integrity. The authors analyze t

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44

Krut, M. V. "Forecasting the phytosanitary state of agrocenoses: innovation developments." Interdepartmental Thematic Scientific Collection of Phytosanitary safety, no. 69 (March 14, 2024): 338–47. https://doi.org/10.36495/phss.2023.69.338-347.

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Goal. Development and implementation of new theories, methods, technologies and models that would allow more reliable forecasting of the development of pests and pathogens of agricultural crops. Methods. Analysis of innovative development of the Institute of Plant Protection of the National Academy of Agrarian Sciences of Ukraine and other institutions of the Scientific and Methodological Center «Protection of Plants» for 2001—2020. Isolation of those relating to the problem forecasting the development of pests and pathogens of plant diseases in agrocenoses of Ukraine. Results. The concept of

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45

Krut, M. V. "Forecasting the phytosanitary state of agrocenoses: innovation developments." Interdepartmental Thematic Scientific Collection of Phytosanitary safety, no. 69 (March 14, 2024): 338–47. http://dx.doi.org/10.36495/1606-9773.2023.69.338-347.

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Goal. Development and implementation of new theories, methods, technologies and models that would allow more reliable forecasting of the development of pests and pathogens of agricultural crops. Methods. Analysis of innovative development of the Institute of Plant Protection of the National Academy of Agrarian Sciences of Ukraine and other institutions of the Scientific and Methodological Center «Protection of Plants» for 2001—2020. Isolation of those relating to the problem forecasting the development of pests and pathogens of plant diseases in agrocenoses of Ukraine. Results. The

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46

Dranko, Oleg, and Natalia Maslyakova. "The Forecasting Express-Model of the Energy Companies’ Financial State." E3S Web of Conferences 209 (2020): 06003. http://dx.doi.org/10.1051/e3sconf/202020906003.

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The paper focuses on a forecasting express-model of the financial state of an enterprise. This model includes forecasting the Profit&Loss statement and the Balance Sheet of the organization. The combination of these documents allows forecasting cash flow in an indirect form. We use open data on the financial statements of individual organizations. Estimates of the cash flow deficit have been carried out both for the power generation sub-sector as a whole and for the largest power generating organizations in Russia.

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47

S., Sarifah Radiah Shariff, Azuan Suhaimi Mohd, Meriam Zahari Siti, and Derasit Zuraidah. "Alternative Methods for Forecasting Variations in Hospital Bed Admission." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 410–16. https://doi.org/10.11591/ijeecs.v9.i2.pp410-416.

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The Malaysian healthcare system is well-being recognized for providing a wide range of access to primary healthcare. The number of hospitals is found to be growing in line with the increase in population. However, overcrowding has become the most common scene that people see in every hospital. The number of patients being admitted may somehow mislead healthcare planners, and thus causing them to underestimate the resources that are required within the hospital. Thus, this study aims to identify better forecasting models for variations in hospital bed admission considering State Space Model (SS

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48

Liu, Wen-Jie, Yu-Ting Bai, Xue-Bo Jin, Ting-Li Su, and Jian-Lei Kong. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting." Mathematics 10, no. 17 (2022): 3188. http://dx.doi.org/10.3390/math10173188.

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Time series forecasting provides a vital basis for the control and management of various systems. The time series data in the real world are usually strongly nonstationary and nonlinear, which increases the difficulty of reliable forecasting. To fully utilize the learning capability of machine learning in time series forecasting, an adaptive broad echo state network (ABESN) is proposed in this paper. Firstly, the broad learning system (BLS) is used as a framework, and the reservoir pools in the echo state network (ESN) are introduced to form the broad echo state network (BESN). Secondly, for t

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49

Chistilina, A. S. "Genesis and Current State of the Forensic Prediction Theory." Actual Problems of Russian Law 19, no. 6 (2024): 156–66. http://dx.doi.org/10.17803/1994-1471.2024.163.6.156-166.

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Predictive activities in the field of forensic science are one of the most relevant practice-oriented areas of research within the framework of forensic science. Predicting development trends of any subject, process, and system based on existing information about them and the achieved level of science allows us to obtain advanced knowledge that is relevant given the high speed of emergence, processing and use of information in modern society. The digitalization process causes the emergence of new objects and new tasks of expert research, changes in their methods and methodology, which signific

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50

Trierweiler Ribeiro, Gabriel, João Guilherme Sauer, Naylene Fraccanabbia, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting." Energies 13, no. 9 (2020): 2390. http://dx.doi.org/10.3390/en13092390.

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Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies. The low computational cost and powerful performance of ESN make it widely used in a range of applications including forecasting tasks and nonlinear modeling. This paper presents a Bayesian optimization algorithm (BOA) of ESN hyperparameters in load forecasting with its main contributions including helping the selection of optimization algorithms for tu

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