Correct Forecasting Research Articles

Challenges and prospects of preterm birth prediction in multiple pregnancies

For today, twins make up about 1.5% of the population of our planet. It is more than one hundred million people, which in number corresponds to the population of two Frances. The number of twins born relative to the total number of newborns in different countries and on different continents is different, but the overall trend is that it continues to grow. In recent years, the percentage of multiple pregnancy has increased almost 2.5 times, which is associated with the widespread use of assisted reproductive technologies.At the same time, pregnancy in multiple births is an extremely important problem in modern obstetrics, as it is accompanied by a high level of complications for both the mother and the fetuses. Multiple pregnancy contributes significantly to the formation of adverse perinatal outcomes, which is primarily due to the high rate of preterm birth. Premature twins are at high risk of neurological and neuropsychiatric disorders, respiratory distress, endocrine and metabolic disorders, which subsequently become the cause of disability and social maladaptation of children. In this regard, the reduction in the number of premature births is today a priority task, the solution of which is possible only through timely and correct forecasting. The multifactority of pathogenic mechanisms determines the necessity of diagnostic search strategies that can identify markers of various pathogenetic ways of preterm birth. (For citation: Kosyakova OV, Bespalova ОN. Challenges and prospects of preterm birth prediction in multiple pregnancies. Journal of Obstetrics and Women’s Diseases. 2018;67(4):48-59. doi: 10.17816/JOWD67448-59).

A Study of Time Series Models ARIMA and ETS

The aim of the study is to introduce some approach which might help in improving daily temperature of data. Weather is a natural a phenomenon for which forecasting is a great challenge today. Weather parameters such as Rainfall, Relative Humidity , Wind Speed, Air Temperature are highly non-linear and complex phenomena, which include mathematical simulation and modeling for its correct forecasting. Weather Forecasting is use to simplify the purpose of knowledge and tools that are used for the state of atmosphere at a given place. The prediction is becoming more complicated due to changing weather condition. There are different software and types are available for Time Series forecasting. Our aim is to analyze the parameters and do the comparison of some strategies in predicting these temperatures. Here we tend to analyze the data of given parameters and notice the prediction for few period using the strategy of Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS).The data from meteorological centers are taken for comparison of methods using packages such as ggplot2, forecast, time Date in R and automatic prediction strategies are available within the package applied for modeling with ARIMA and ETS methods. On basis of accuracy we tend to attempt the simplest Methodology. Our model will compare on basis of MAE, MASE, MAPE AND RMSE. The identification of model will chromatic inspection of both the ACF and PACF to hypothesize many possible models will estimated by selection criteria AIC, AICc and BIC.

Stochastic Differential Games for Which the Open-Loop Equilibrium is Subgame Perfect

It is generally admitted that a correct forecasting of uncertain variables needs Markov decision rules. In a dynamic game environment, this belief is reinforced if one focuses on credible actions of the players. Usually, subgame perfectness requires equilibrium strategies to be constructed on Markov rules. It comes as a surprise that there are interesting classes of stochastic differential games where the equilibrium based on open-loop strategies is subgame perfect. This fact is well known for deterministic games. We explore here the stochastic case, not dealt with up to now, identifying different game structures leading to the subgame perfectness of the open-loop equilibrium.

Spatial and temporal evolution of climatic factors and its impacts on potential evapotranspiration in Loess Plateau of Northern Shaanxi, China

Agriculture is very sensitive to climate change, and correct forecasting of climate change is a great help to accurate allocation of irrigation water. The use of irrigation water is influenced by crop water demand and precipitation. Potential evapotranspiration (ET0) is a measure of the ability of the atmosphere to remove water from the surface through the processes of evaporation and transpiration, assuming no control on water supply. It plays an important role in assessing crop water requirements, regional dry–wet conditions, and other factors of water resource management. This study analyzed the spatial and temporal evolution processes and characteristics of major meteorological parameters at 10 stations in the Loess Plateau of northern Shaanxi (LPNS). By using the Mann–Kendall trend test with trend–free pre–whitening and the ArcGIS platform, the potential evapotranspiration of each station was quantified by using the Penman–Monteith equation, and the effects of climatic factors on potential evapotranspiration were assessed by analyzing the contribution rate and sensitivity of the climatic factors. The results showed that the climate in LPNS has become warmer and drier. In terms of the sensitivity of ET0 to the variation of each climatic factor in LPNS, relative humidity (0.65) had the highest sensitivity, followed by daily maximum temperature, wind speed, sunshine hours, and daily minimum temperature (−0.05). In terms of the contribution rate of each factor to ET0, daily maximum temperature (5.16%) had the highest value, followed by daily minimum temperature, sunshine hours, relative humidity, and wind speed (1.14%). This study provides a reference for the management of agricultural water resources and for countermeasures to climate change. According to the climate change and the characteristics of the study area, farmers in the region should increase irrigation to guarantee crop water demand.

Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors

Sale and purchase of the growing competition between companies in Indonesian, make every company should have a proper planning in order to win the competition with other companies. One of the things that can be done to design the plan is to make car sales forecast for the next few periods, it’s required that the amount of inventory of cars that will be sold in proportion to the number of cars needed. While to get the correct forecasting, on of the methods that can be used is the method of Adaptive Spline Threshold Autoregression (ASTAR). Therefore, this time the discussion will focus on the use of Adaptive Spline Threshold Autoregression (ASTAR) method in forecasting the volume of car sales in PT.Srikandi Diamond Motors using time series data.In the discussion of this research, forecasting using the method of forecasting value Adaptive Spline Threshold Autoregression (ASTAR) produce approximately correct.

Structures of Stochastic Differential Games for Which the Open-Loop Equilibrium Is Subgame Perfect

It is generally admitted that a correct forecasting of uncertain variables needs Markov decision rules. In a dynamic game environment, this belief is reinforced if one focus on credible actions of the players. Usually, subgame perfectness requires equilibrium strategies being constructed on Markov rules. It comes as a surprise that there are interesting classes of stochastic differential games where the equilibrium based on open loop strategies is subgame perfect. This fact is well known for deterministic games. We explore here the stochastic case, not dealt with up to now, identifying different game structures leading to the subgame perfectness of the open-loop equilibrium.

Neural Network - An Application to the Food Production Data

In industry, making a correct forecasting is a very important matter. If the correct forecasting is not executed, there arise a lot of stocks and/or it also causes lack of goods. Time series analysis, neural networks and other methods are applied to this problem. In this paper, neural network is applied and Multilayer perceptron Algorithm is newly developed. The method is applied to the food production data of prepared frozen foods. When there is a big change of the data, the neural networks cannot learn the past data properly, therefore we have devised a new method to cope with this. Repeating the data into plural section, smooth change is established and we could make a neural network learn more smoothly. Thus, we have obtained good results. The result is compared with the method we have developed before. We have obtained the good results.

Demand forecasting, Economic Order Quantity and inventory costs calculation of a company producing solar panels

Forecasting accuracy drives the performance of inventory management. This study involves forecasting the sales of PV modules of a solar panel manufacturing company by demand forecasting methods including regression and exponential smoothing models. Demand forecasted is used to control inventory, minimize cost of ordering and holding inventory. The model should propose a viable economic order quantity which reduces the total cost of the plan. I. Introduction ORRECT forecasting has become a need for survival in today's business world. Not only for production unit but correct forecasting is required at every stage of product development. It is a foundation upon which the all company plan and objectives are built in terms of market and revenue. Correct forecasting helps save wastage in materials, man hours, machine idle time etc which leads to increased efficiency and productivity. Exponential smoothening and regression models have been potently used as they are the most cost effective statistical tools. With the advent of rapid industrialization, increased locomotives and energy needs, there is a need to shift to renewable forms of energy to fulfill our energy needs. Thus, the dataset selected is that of Solar Panel which under general consensus states the rising demand of solar panels signifies the rising acceptance of using solar energy as a viable alternative energy source. Solar Panel refers to a panel designed to absorb the sun's rays which is an ultimate source of energy for generating electricity or heat. The factor that has hampered the production of solar panels is the high installation and starting cost but as the acceptance of solar panels rises the cost of setting up solar panels has been seen to be decreasing. From the past demand curves, as stated by Swanson's law which states that with every doubling of production of panels, there has been a 20 percent reduction in the cost of panels, which holds true within the limit of error.

The validation of SSWs forecasts in atmospheric global circulation models

While the influence of the troposphere on the stratosphere is well known, recent observational and modeling studies have demonstrated that the stratosphere has an impact on the troposphere as well. The dynamical coupling between stratosphere and troposphere is particularly strong during sudden stratospheric warming (SSW) events. The correct forecasting of the onset and duration of SSW events is therefore important and is a current challenge for weather forecasting centers. As there is a lack of observations in the upper stratosphere with good temporal and spatial coverage, additional techniques may be helpful to constrain SSWs. This is illustrated using volcanic infrasound measurements. The observations are compared with nowcast and forecast models up to 10 days. While a general agreement is found during the summer period, larger discrepancies are found during the equinox and major SSW of January 2013.

Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China

Research Article| June 18 2014 Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China Wenxi Lu; Wenxi Lu 1College of Environment and Resources, Jilin University, Changchun 130021, China and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China E-mail: luwenxi@jlu.edu.cn Search for other works by this author on: This Site PubMed Google Scholar Haibo Chu; Haibo Chu 1College of Environment and Resources, Jilin University, Changchun 130021, China and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China Search for other works by this author on: This Site PubMed Google Scholar Zheng Zhang Zheng Zhang 1College of Environment and Resources, Jilin University, Changchun 130021, China and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2015) 64 (1): 95–104. https://doi.org/10.2166/aqua.2014.002 Article history Received: December 25 2013 Accepted: May 23 2014 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Wenxi Lu, Haibo Chu, Zheng Zhang; Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China. Journal of Water Supply: Research and Technology-Aqua 1 February 2015; 64 (1): 95–104. doi: https://doi.org/10.2166/aqua.2014.002 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Rainfall is a key part of the hydrological cycle, and correct forecasting of rainfall is vital in the planning and management of water resources. Generalized regression neural network (GRNN) and support vector regression (SVR) were both applied to forecast monthly rainfall, and the conventional autoregressive model was built for comparison. Furthermore, Akaike Information Criteria were used to identify the proper inputs for the rainfall forecasting model. The data sets of monthly rainfall for a 53-year period from 1957 to 2010 in western Jilin Province, China, were used. The results indicated that the proper inputs would help in effectively improving the prediction accuracy. Furthermore, the results showed that both the SVR and the GRNN model performed better than the autoregressive model in forecasting monthly rainfall. SVR models outperformed all other models during the testing period in terms of the mean absolute error, root-mean-square error, coefficient of efficiency and R2. Therefore, SVR models were applied to forecast monthly rainfall for six cities including Baicheng, Qianguo, Fuyu, Qian'an, Changling and Tongyu. generalized regression neural network, monthly rainfall forecasting, support vector regression, western Jilin Province © IWA Publishing 2015 You do not currently have access to this content.

Investigating the Ability of Artificial Neural Network (ANN) Models to Estimate Missing Rain-gauge Data

The correct forecasting of unrecorded data could be enormously helpful in designing water projects and preventing related damages. The conventional methods available for rainfall estimation usually take a long time to estimate the missing data, and their estimations may have many errors in the long-term simulations. In this study, the capabilities of different Artificial Neural Networks (ANNs) were analyzed in estimating missing data from the Ardabel plain rain gauge stations located in northwestern Iran. Accordingly, six different structures of ANNs were used, and their efficiencies in terms of the mean squared error, training, and validation determination coefficients to select better-estimated missing data were examined. The results revealed that the best model is composed of the feed-forward networks, trained by the Levenberg-Marquardt algorithm and considering only one hidden layer. For each of the stations with a complete data set, an ANN was trained. Data gaps from other stations were obtained by the proposed ANN models. Furthermore, an integrated ANN was developed to investigate the hidden spatial relationships among the rainfall data of the stations as well as temporal auto-correlations. The results indicated the superiority of the proposed integrated model. After the estimation of the rain data gaps, the K-means clustering method was also employed as a data pre-processing method to improve the accuracy of the estimation, and the method led to better results.

Earthquake forecasting: a possible solution considering the GPS ionospheric delay

Abstract. The recent earthquakes in L'Aquila (Italy) and in Japan have dramatically emphasized the problem of natural disasters and their correct forecasting. One of the aims of the research community is to find a possible and reliable forecasting method, considering all the available technologies and tools. Starting from the recently developed research concerning this topic and considering that the number of GPS reference stations around the world is continuously increasing, this study is an attempt to investigate whether it is possible to use GPS data in order to enhance earthquake forecasting. In some cases, ionospheric activity level increases just before to an earthquake event and shows a different behaviour 5–10 days before the event, when the seismic event has a magnitude greater than 4–4.5 degrees. Considering the GPS data from the reference stations located around the L'Aquila area (Italy), an analysis of the daily variations of the ionospheric signal delay has been carried out in order to evaluate a possible correlation between seismic events and unexpected variations of ionospheric activities. Many different scenarios have been tested, in particular considering the elevation angles, the visibility lengths and the time of day (morning, afternoon or night) of the satellites. In this paper, the contribution of the ionospheric impact has been shown: a realistic correlation between ionospheric delay and earthquake can be seen about one week before the seismic event.

Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems

Effective management of district heating networks depends upon the correct forecasting of heat consumption during a certain period. In this work short-term forecasting for the amount of heat consumption is performed first to validate the three forecasting methods: partial least squares (PLS) method, artificial neural network (ANN), and support vector regression (SVR) method. Based on the results of short-term forecasting, one-week ahead forecasting was performed for the Suseo district heating network. Data of heat consumption and ambient temperature during January and February in 2007 and 2008 were employed as training elements. The heat consumption estimated was compared with actual one in the Suseo area to validate the forecasting models.

Strategic behaviour and the law: how legal authorities deal with factual strategic behaviour of former monopolists

In most of the western world and to some extent elsewhere, public utility provision is moving rapidly towards privatisation, liberalisation and marketisation. The end stage of all these restructuring processes is invariably expected to be some form of healthy competition. But former monopolists, whilst officially hailing competition, may apply technical standards, traditional social links with the public sector and information advantages to keep new entrants at bay. This conduct is called strategic behaviour. Strategic behaviour is quite understandable and even rational from the point of view of the actor and is not necessarily always wrong from a moral or wider societal perspective. Wronged parties often feel inclined to invoke regulators or legal authorities to remedy their predicament and correct the perceived detrimental strategic behaviour. Yet quite often, regulatory and legal authorities have no easy job dealing with such conduct, for lack of substantial information on the matters at hand. In this paper, therefore, the main question is, How do regulatory and legal authorities deal with strategic behaviour in the evolving market structures in liberalised utility sectors? Legal theory, in order to get to concrete judgements of right and wrong, has just two binary concepts to come to final decisions: legal (unmistakably in agreement with the law) or illegal (unmistakably in conflict with the law). Legal authorities or institutions, such as regulators, arriving at legally relevant decisions are therefore said to digitise the analogue world of social perception. Greyish categories such as strategic behaviour can acquire no meaning in this context. In the case at hand, the regulator experienced quite serious difficulties in pinpointing the nature of the strategic behaviour and see-sawed for a long time before undertaking legal action. Once it did so, it transformed a technical issue on interconnection capacity to a procedural issue of correct forecasting, therefore changing and perhaps to some extent limiting the options parties had for strategic behaviour.

Monitoring Fertilizer Supply and Demand in a Deregulated Market

The fertilizer sector in Pakistan is on the verge of fundamental changes. Some barriers have been removed and others will follow. The process of privatization has greatly increased the need for monitoring supplies, prices and stocks. This paper describes the importance of continued monitoring of the fertilizer supply and demand situation and prices. This must be undertaken on a regular basis because while fertilizer consumption is purely seasonal, yet we continue to depend on imports of phosphatic and potassic fertilizer. Moreover, importation procedures take time. This paper also places emphasis on the correct forecasting of fertilizer demand, to avoid shortages for the farmers, or excess stocks which would yield low profits for the distributors.

The Theory of Public Utility Pricing and Its Application

In the 1930's and 1940's, the view came to be held that the right policy was to make public utility prices everywhere equal to marginal cost, even where marginal cost was less than average cost and a government subsidy was required to maintain production. This policy proposal had serious weaknesses. It did not take into account the stimulus to correct forecasting of having a subsequent market test whether consumers were willing to pay the total cost; it ignored the probably effects on the administrative structure, with state enterprise and centralized operations superceding decentralized operations; it involved a redistribution of income in favor of consumers of products produced in conditions of decreasing costs; it failed to take into account the misallocation of resources resulting from the additional taxation necessitated by the subsidies.

Cobweb Cycles and Partially Correct Forecasting

TN A notable article, published some ten years ago, Emile Grunberg and Franco Modigliani proved the possibility and the conditions of successful forecasting in cases where the actors on the scene are influenced by the public forecasts.' In addition to a generalized exposition they exemplified their ideas in the concrete frame of price prediction in a cobweb setting.2 In a later paper Devletoglou showed how the situation is changed and how the course of events is altered once efficient forecasting has been introduced.3 The present paper takes the discussion a small step further by investigating the consequences of introducing the more realistic assumption of less-than-perfect forecasting. In sections 2 and 3 the cobweb situation without and with correct public forecasting is briefly summarized to serve as a basis of comparison for section 4, where partially correct forecasting is introduced. Section 5 contains a numerical comparison of all three cases. The notation and exposition used in the following sections are somewhat different from those employed by Devletoglou (who in turn deviates from the GrunbergModigliani notation), but the results in section 3 are essentially the same as those reached by him.4

Correct Public Prediction and the Stability of Equilibrium

Previous articleNext article No AccessCorrect Public Prediction and the Stability of EquilibriumEvangelos A. DevletoglouEvangelos A. Devletoglou Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Journal of Political Economy Volume 69, Number 2Apr., 1961 Article DOIhttps://doi.org/10.1086/258450 Views: 4Total views on this site Citations: 9Citations are reported from Crossref Copyright 1961 The University of ChicagoPDF download Crossref reports the following articles citing this article:Eva R. Porras Contributors to the Bubble Formation and Contagion Process, (Jan 2016): 55–89.https://doi.org/10.1057/9781137358769_3Antonella Rancan Modigliani’s and Simon’s Early Contributions to Uncertainty (1952–61), History of Political Economy 45, no.11 (Mar 2013): 1–38.https://doi.org/10.1215/00182702-1965177Paul Dragos Aligica Social predictions, institutional design and prestige loops: Richard Henshel’s contribution to futures studies, Futures 41, no.33 (Apr 2009): 147–155.https://doi.org/10.1016/j.futures.2008.09.012Antonella Rancan Modigliani's Contribution to the 'Carnegie Research Agenda', SSRN Electronic Journal (Jan 2009).https://doi.org/10.2139/ssrn.1477130Keen Meng Choy, Kenneth Leong, Anthony S. Tay Non-fundamental expectations and economic fluctuations: Evidence from professional forecasts, Journal of Macroeconomics 28, no.22 (Jun 2006): 446–460.https://doi.org/10.1016/j.jmacro.2004.07.004Richard L. Henshel The Grünberg/Modigliani and Simon possibility theorem: A social psychological critique, The Journal of Socio-Economics 24, no.33 (Sep 1995): 501–520.https://doi.org/10.1016/1053-5357(95)90020-9Richard L. Henshel Do self-fulfilling prophecies improve or degrade predictive accuracy? How sociology and economics can disagree and both be right, The Journal of Socio-Economics 22, no.22 (Jun 1993): 85–104.https://doi.org/10.1016/1053-5357(93)90017-FK. Holden, D.A. Peel Expectations formation, public forecasts and the wage equation, Economic Modelling 3, no.22 (Apr 1986): 129–134.https://doi.org/10.1016/0264-9993(86)90005-2 K. W. Rothschild Cobweb Cycles and Partially Correct Forecasting, Journal of Political Economy 72, no.33 (Oct 2015): 300–305.https://doi.org/10.1086/258909