A weather forecast model accuracy analysis and ECMWF enhancement proposal by neural network

This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people's everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.Web of Science1923art. no. 514

Cluster analysis of the economic activity of Slovak companies regarding potential indicators of earnings management

Research background: All over the world, any information about the earnings manipulation is very important for all the stakeholders of the companies. Therefore, it is necessary to detect this situation in a certain way. The global practice has shown that it is appropriate to create detection models and it would be very useful to specify individual sectors or the groups of sectors of economic activities of companies. Purpose of the article: The article aims to the financial ratios of Slovak companies that are globally used in the detection of earnings management. Based on hierarchical cluster analysis we identify groups of economic activities (according to the international NACE classification) with similar financial characteristics. Methods: For efficient earnings manipulation detection, high-quality and up-to-date financial data is required. We used financial data of real Slovak companies from the year 2018 obtained from international database Amadeus. After a precise pre-preparation of the dataset, we use the standard clustering procedures. Using the analysis of the dendrogram, the groups of the companies with their economic activities are identified. Findings & Value added: The results of the analysis show that there exist logical groups of NACE categories of economic activity of companies with similar characteristics. Regarding potential earnings manipulation, companies in these groups are as similar as possible. Therefore, financial characteristics can be analyzed together, and more accurate detection models could be created for them

Being an outlier: a company non-prosperity sign?

Research background: The state of financial distress or imminent bankruptcy are very difficult situations that the management of every company wants to avoid. For these reasons, prediction of company bankruptcy or financial distress has been recently in a focus of economists and scientists in many countries over the world. Purpose of the article: Various financial indicators, mostly financial ratios, are usually used to predict the financial distress. In order to create a strong prediction model and a statistically significant prediction of bankruptcy, it is advisable to use a deep statistical analysis of the data. In this paper, we analysed the real financial ratios of Slovak companies from the year 2017. In the phase of data preparation for further analysis, we checked the existence of outliers and found that there are some companies that are multivariate outliers because are significantly different from other companies in the database. Thus, we deeply focused on these outlying companies and analysed whether to be an outlier is a sign of financial distress. Methods: We analysed whether there are much more non-prosperous companies in the set of outlier companies and if their financial indicators are significantly different from those of the prosperous companies. For these analyses, we used testing of the statistical hypotheses, such as the test for equality of means and chi-square test. Findings & Value added: The ratio of non-prosperous companies between the outliers is significantly higher than 50 % and the attributes of non-prosperity and being an outlier are dependent. The means of almost all financial ratios of prosperous and non-prosperous companies among outliers are significantly different

3235 Verification of window properties after 10 years of exploitation: results of measurements in the pavilion laboratory and the climate chamber

The article will deal with the analysis of measured data on a plastic window with thermal insulating triple glazing, which is suitable for low-energy or passive houses. The window was installed in 2011 in the test laboratory of the Department of Building Engineering and Urban planning, Faculty of Civil Engineering, University of Žilina (Slovakia), where it was tested under standard indoor climate conditions and real outdoor climate conditions. Surface temperatures on the frame friezes and glass system and heat flux density were recorded at a five-minute time step. In 2020, the window was removed from the laboratory and subsequently tested in a climate chamber. This paper will present the results of these measurements in terms of heat flow density waveforms, heat transfer coefficient, and total solar transmittance through the glazing. Subsequently, a simulation model of this window will be created in the environment of a computational program and its verification based on the measurements will be carried out. A series of calculations will be performed on the tuned model and analyses of the results and comparisons will be presented under the same climatic conditions as during the real measurements recorded by the meteorological station

Experimental Analysis of Thermo-Technical Parameters of Windows Glazing in the Pavilion Laboratory

Improving the energy performance of buildings in the context of external climatic conditions and the requirements of indoor environments is a hot topic in the construction industry. It primarily concerns reducing the energy used for heating and cooling in buildings. In the EU sector, this is addressed by the Energy Performance Directive (EPBD), which is followed by relevant national standards. The energy performance of buildings is strongly influenced by the window structures that are part of the building envelope. Their influence on energy performance is represented by the heat transfer coefficient, which differs in the actual built-in window construction from the design value given by the manufacturer. In this paper, the authors deal with its measurement in situ using the heat flux measurement method. The measurement was carried out in the pavilion laboratory of the Department of Building Engineering and Urban Planning (DBEUP), Faculty of Civil Engineering (FCE), University of Zilina (UNIZA), on three window constructions of different material bases. During the measurements, surface temperatures on the glazing, heat flux density, and air temperatures were recorded in minute increments. The influence of the year-round cycle of the outdoor environment on the embedded window structures is presented and the results are presented in the conclusion of the paper

Numerical Analysis of a Poor-Quality Glazing System in a Computer Software

Following the current topic in the construction industry of improving energy performance of buildings, this paper discusses a numerical analysis of a glazing system. The performed analysis is based on the results of a particular type of glazing during measurements in a pavilion laboratory and in a set of climatic chambers which have been the focus of previous research papers. Fifteen glazing cases were made in the analysis and their glazing properties were monitored - the filling gas and the position of the low-emissivity layers were changed and observed. The results indicate that the values obtained from experimental measurements are greatly influenced by a degraded or missing low-emissivity layer and a missing or incorrect filler gas

Logit business failure prediction in V4 countries

The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders

Prediction of Default of Small Companies in the Slovak Republic

From the time of Altman and the first bankruptcy prediction models, the prediction of default of companies is in the centre of interest of many economists and scientists all over the world. For companies, early detection of the possible threat of imminent financial difficulties or even bankruptcy is a very important part of financial analysis. Over the last few years, many predictive models have been created in the world. However, it has been shown that these models are not very well transferable to the conditions of the economy of another country and their prediction or rating power in another country is lower. Therefore, it is best to create a specific predictive model in the country that takes into account the situation of companies on the basis of real data on their financial situation. This paper is focused on creating a model of failure prediction of small companies in Slovakia using a well-known and widely used method of multivariate discriminant analysis. Discriminant analysis is one of the oldest multivariate statistical methods and sometimes it is difficult to fulfil certain assumptions for data. However, its results are easily interpretable and can be used to classify a company to the group of companies with risk of financial difficulties or, on the contrary, between well-prosperous companies. Prediction model is created based on real data on Slovak enterprises and has a strong classification ability in the specific conditions of the Slovak Republic

Logit business failure prediction in V4 countries

The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders

Decision tree based model of business failure prediction for Polish companies

Research background: The issue of predicting the financial situation of companies is a relatively young field of economic research. Its origin dates back to the 30's of the 20th century, but constant research in this area proves the currentness of this topic even today. The issue of predicting the financial situation of a company is up to date not only for the company itself, but also for all stakeholders. Purpose of the article: The main purpose of this study is to create new prediction models by using the method of decision trees, in achieving sufficient prediction power of the generated model with a large database of real data on Polish companies obtained from the Amadeus database. Methods: As a result of the development of artificial intelligence, new methods for predicting financial failure of the company have been introduced into financial prediction analysis. One of the most widely used data mining techniques in this field is the method of decision trees. In the paper, we applied the CART and CHAID approach to create a model of predicting the financial difficulties of Polish companies. Findings & Value added: For the creation of the prediction model, a total of 37 financial and economic indicators of Polish companies were used. The resulting decision trees based prediction models for Polish companies reach a prediction power of more than 98%. The success of the classification for non-prosperous companies is more than 83%. The created decision tree-based prediction models are useful mainly for predicting the financial difficulties of Polish companies, but can also be used for companies in another country