ПРАВИЛА ПОЛІТИКИ УРЯДУ У ФІНАНСУВАННІ ОСВІТИ: ІДЕНТИФІКАЦІЯ НА ОСНОВІ НЕЙРОННИХ МЕРЕЖ

Радіонова І.Ф., Усик В.І. ПРАВИЛА ПОЛІТИКИ УРЯДУ У ФІНАНСУВАННІ ОСВІТИ: ІДЕНТИФІКАЦІЯ НА ОСНОВІ НЕЙРОННИХ МЕРЕЖМета. Просування у вирішенні проблеми ідентифікації правил фінансування освіти та використання потенціалу методу штучних нейронних мереж в процесі ідентифікації правил.Методика дослідження. У процесі дослідження використано такі конкретні методи: системний підхід – для класифікації форм ідентифікації правил політики та визначення рівнів, на яких мали б реалізуватись правила фінансування освіти; аналізу на основі штучних нейронних мереж – для обґрунтування та ідентифікації правил фінансування освітньої сфери; формалізації та графічного представлення отриманих результатів – для унаочнення результатів дослідження.Результати дослідження. Визначено, що правила політики уряду є інструментом публічного управління, а для використання в цій якості вони мають бути ідентифіковані та інституціоналізовані. Обґрунтовано, що використання методу штучних нейронних мереж, який дозволяє аналізувати та прогнозувати нелінійні процеси з високою невизначеністю, створює нові можливості ідентифікації правил політики. Здійснено прогнозування двох показників фінансування освіти, формування яких мало б підпорядковуватись правилам уряду, на основі двох нейронних мереж.Наукова новизна результатів дослідження. Запропоновано інноваційний підхід до моделювання та прогнозування показників фінансування української освіти, як підпорядкованих певним правилам уряду, з використанням моделей штучних нейронних мереж. Це дало змогу отримати графи зв’язків між вхідними та вихідними змінними та передбачити показники частки витрат на освіту у ВВП та частки витрат на освіту у державних витратах, за умови реалізації передбачуваних урядом, міжнародними установами та науково-дослідними установами країни п’ятьох вхідних змінних, а саме: продуктивності праці, частки інноваційної діяльності, частки дефіциту бюджету та державного боргу у ВВП, частки працездатного населення у населенні країни.Практична значущість результатів дослідження. Розроблені та запропоновані авторами практичні підходи до ідентифікації правил фінансування освіти можуть сприяти досягненню вищого рівня інституціоналізації правил та використовуватися в роботі Міністерства освіти і науки.Ключові слова: правила політики уряду; правила фінансування освіти; публічне управління; штучні нейронні мережі. Радионова И.Ф., Усик В.И. ПРАВИЛА ПОЛИТИКИ ПРАВИТЕЛЬСТВА ОТНОСИТЕЛЬНО ФИНАНСИРОВАНИЯ ОБРАЗОВАНИЯ: ИДЕНТИФИКАЦИЯ НА ОСНОВЕ НЕЙРОННЫХ СЕТЕЙЦель. Продвижение в решении проблемы идентификации правил финансирования образования и использования потенциала метода искусственных нейронных сетей в процессе идентификации правил.Методика исследования. В процессе исследования использованы следующие конкретные методы: системный подход ‒ для классификации форм идентификации правил политики и определения уровней, на которых должны реализоваться правила финансирования образования; анализа на основе искусственных нейронных сетей ‒ для обоснования и идентификации правил финансирования образовательной сферы; формализации и графического представления полученных результатов ‒ для наглядности результатов исследования.Результаты исследования. Определено, что правила политики правительства являются инструментом публичного управления, а для использования в этом качестве они должны быть идентифицированы и институционализированы. Обосновано, что использование метода искусственных нейронных сетей, позволяющего анализировать и прогнозировать нелинейные процессы с высокой неопределенностью, создает новые возможности идентификации правил политики. Осуществлено прогнозирование двух показателей финансирования образования, формирование которых должно подчиняться правилам правительства, на основе двух нейронных сетей.Научная новизна результатов исследования. Предложен инновационный подход к моделированию и прогнозированию показателей финансирования украинского образования, как подчиненных определенным правилам правительства, с использованием моделей искусственных нейронных сетей. Это позволило получить графы связей между входными и выходными переменными и предусмотреть показатели доли расходов на образование в ВВП и доли расходов на образование в государственных расходах, при условии реализации предполагаемых правительством, международными учреждениями и научно-исследовательскими учреждениями страны пяти входных переменных, а именно: производительности труда, доли инновационной деятельности, доли дефицита бюджета и государственного долга в ВВП, доли трудоспособного населения среди всего населении страны.Практическая значимость результатов исследования. Разработаны и предложены авторами практические подходы к идентификации правил финансирования образования могут способствовать достижению высшего уровня институционализации правил и использоваться в работе Министерства образования и науки.Ключевые слова: правила политики правительства; правила финансирования образования; публичное управление; искусственные нейронные сети. Radionova I.F., Usyk V.I. REGULATIONS OF GOVERNMENT POLICY IN FINANCING EDUCATION: IDENTIFICATION BASED ON NEURAL NETWORKSPurpose. The aim of the article is a progress in addressing the problem of identifying educational funding rules and harnessing the potential of artificial neural networks in the process of identifying rules.Methodology of research. The following specific methods have been used in the research process: a systematic approach – to classify forms of identification of policy rules and to determine the levels at which educational funding rules should be implemented; artificial neural network analysis – to substantiate and identify educational funding rules; formalization and graphical presentation of the results obtained – to refine the results of the study.Findings. It has been determined that government policy rules are a tool of public administration, and to be used in this capacity they must be identified and institutionalized. It is substantiated that the use of artificial neural network method, which allows to analyze and predict nonlinear processes with high uncertainty, creates new opportunities for identification of policy rules. Two funding indicators for education, the formation of which should be governed by government rules, are based on two neural networks.Originality. An innovative approach to modelling and forecasting of Ukrainian education funding indicators, subject to certain government rules, using artificial neural network models, is proposed. This made it possible to obtain graphs of the relationship between input and output variables and to predict the share of education expenditure in GDP and the share of education expenditure in public spending, provided that the five governmental variables envisaged by the government, international institutions and research institutions of the country, namely: labour productivity, share of innovative activity, share of budget deficit and public debt in GDP, share of able-bodied population in the country's population.Practical value. Practical approaches to the identification of educational funding rules developed and proposed by the authors can help to achieve a higher level of institutionalization of the rules and be used in the work of the Ministry of Education and Science.Key words: rules of government policy; rules for financing education; public administration; artificial neural networks

A contribution to exchange rate forecasting based on machine learning techniques

El propòsit d'aquesta tesi és examinar les aportacions a l'estudi de la predicció de la taxa de canvi basada en l'ús de tècniques d'aprenentatge automàtic. Aquestes aportacions es veuen facilitades i millorades per l'ús de variables econòmiques, indicadors tècnics i variables de tipus ‘business and consumer survey’. Aquesta investigació s’organitza entorn d’una recopilació de quatre articles. L'objectiu de cadascun dels quatre treballs de recerca d'aquesta tesi és el de contribuir a l'avanç del coneixement sobre els efectes i mecanismes mitjançant els quals l'ús de variables econòmiques, indicadors tècnics, variables de tipus ‘business and consumer survey’, i la selecció dels paràmetres de models predictius són capaços de millorar les prediccions de la taxa de canvi. Fent ús d'una tècnica de predicció no lineal, el primer article d'aquesta tesi es centra majoritàriament en l'impacte que tenen l'ús de variables econòmiques i la selecció dels paràmetres dels models en les prediccions de la taxa de canvi per a dos països. L'últim experiment d'aquest primer article fa ús de la taxa de canvi del període anterior i d'indicadors econòmics com a variables d'entrada en els models predictius. El segon article d'aquesta tesi analitza com la combinació de mitjanes mòbils, variables de tipus ‘business and consumer survey’ i la selecció dels paràmetres dels models milloren les prediccions del canvi per a dos països. A diferència del primer article, aquest segon treball de recerca afegeix mitjanes mòbils i variables de tipus ‘business and consumer survey’ com a variables d'entrada en els models predictius, i descarta l'ús de variables econòmiques. Un dels objectius d'aquest segon article és determinar el possible impacte de les variables de tipus ‘business and consumer survey’ en les taxes de canvi. El tercer article d'aquesta tesi té els mateixos objectius que el segon, però amb l'excepció que l'anàlisi abasta les taxes de canvi de set països. El quart article de la tesi compta amb els mateixos objectius que l'article anterior, però amb la diferència que fa ús d'un sol indicador tècnic. En general, l'enfocament d'aquesta tesi pretén examinar diferents alternatives per a millorar les prediccions del tipus de canvi a través de l'ús de màquines de suport vectorial. Una combinació de variables i la selecció dels paràmetres dels models predictius ajudaran a aconseguir aquest propòsit.El propósito de esta tesis es examinar las aportaciones al estudio de la predicción de la tasa de cambio basada en el uso de técnicas de aprendizaje automático. Dichas aportaciones se ven facilitadas y mejoradas por el uso de variables económicas, indicadores técnicos y variables de tipo ‘business and consumer survey’. Esta investigación está organizada en un compendio de cuatro artículos. El objetivo de cada uno de los cuatro trabajos de investigación de esta tesis es el de contribuir al avance del conocimiento sobre los efectos y mecanismos mediante los cuales el uso de variables económicas, indicadores técnicos, variables de tipo ‘business and consumer survey’, y la selección de los parámetros de modelos predictivos son capaces de mejorar las predicciones de la tasa de cambio. Haciendo uso de una técnica de predicción no lineal, el primer artículo de esta tesis se centra mayoritariamente en el impacto que tienen el uso de variables económicas y la selección de los parámetros de los modelos en las predicciones de la tasa de cambio para dos países. El último experimento de este primer artículo hace uso de la tasa de cambio del periodo anterior y de indicadores económicos como variables de entrada en los modelos predictivos. El segundo artículo de esta tesis analiza cómo la combinación de medias móviles, variables de tipo ‘business and consumer survey’ y la selección de los parámetros de los modelos mejoran las predicciones del cambio para dos países. A diferencia del primer artículo, este segundo trabajo de investigación añade medias móviles y variables de tipo ‘business and consumer survey’ como variables de entrada en los modelos predictivos, y descarta el uso de variables económicas. Uno de los objetivos de este segundo artículo es determinar el posible impacto de las variables de tipo ‘business and consumer survey’ en las tasas de cambio. El tercer artículo de esta tesis tiene los mismos objetivos que el segundo, pero con la salvedad de que el análisis abarca las tasas de cambio de siete países. El cuarto artículo de esta tesis cuenta con los mismos objetivos que el artículo anterior, pero con la diferencia de que hace uso de un solo indicador técnico. En general, el enfoque de esta tesis pretende examinar diferentes alternativas para mejorar las predicciones del tipo de cambio a través del uso de máquinas de soporte vectorial. Una combinación de variables y la selección de los parámetros de los modelos predictivos ayudarán a conseguir este propósito.The purpose of this thesis is to examine the contribution made by machine learning techniques on exchange rate forecasting. Such contributions are facilitated and enhanced by the use of fundamental economic variables, technical indicators and business and consumer survey variables as inputs in the forecasting models selected. This research has been organized in a compendium of four articles. The aim of each of these four articles is to contribute to advance our knowledge on the effects and means by which the use of fundamental economic variables, technical indicators, business and consumer surveys, and a model’s free-parameters selection is capable of improving exchange rate predictions. Through the use of a non-linear forecasting technique, one research paper examines the effect of fundamental economic variables and a model’s parameters selection on exchange rate forecasts, whereas the other three articles concentrate on the effect of technical indicators, a model’s parameters selection and business and consumer surveys variables on exchange rate forecasting. The first paper of this thesis has the objective of examining fundamental economic variables and a forecasting model’s parameters in an effort to understand the possible advantages or disadvantages these variables may bring to the exchange rate predictions in terms of forecasting performance and accuracy. The second paper of this thesis analyses how the combination of moving averages, business and consumer surveys and a forecasting model’s parameters improves exchange rate predictions. Compared to the first paper, this second paper adds moving averages and business and consumer surveys variables as inputs to the forecasting model, and disregards the use of fundamental economic variables. One of the goals of this paper is to determine the possible effects of business and consumer surveys on exchange rates. The third paper of this thesis has the same objectives as the second paper, but its analysis is expanded by taking into account the exchange rates of 7 countries. The fourth paper in this thesis takes a similar approach as the second and third papers, but makes use of a single technical indicator. In general, this thesis focuses on the improvement of exchange rate predictions through the use of support vector machines. A combination of variables and a model’s parameters selection enhances the way to achieve this purpose

Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada's main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model's decisions, which are supported by theoretical considerations

Forest ecosystem changes from annual methane source to sink depending on late summer water balance

Forests dominate the global carbon cycle, but their role in methane (CH4) biogeochemistry remains uncertain. We analyzed whole-ecosystem CH4 fluxes from 2 years, obtained over a lowland evergreen forest in Maine, USA. Gross primary productivity provided the strongest correlation with the CH4 flux in both years, with an additional significant effect of soil moisture in the second, drier year. This forest was a neutral to net source of CH4 in 2011 and a small net sink in 2012. Interannual variability in the summer hydrologic cycle apparently shifts the ecosystem from being a net source to a sink for CH4. The small magnitude of the CH4 fluxes and observed control or CH4 fluxes by forest productivity and summer precipitation provide novel insight into the CH4 cycle in this globally important forest ecosystem.Organismic and Evolutionary Biolog

Online learning in financial time series

We wish to understand if additional learning forms can be combined with sequential optimisation to provide superior benefit over batch learning in various tasks operating in financial time series. In chapter 4, Online learning with radial basis function networks, we provide multi-horizon forecasts on the returns of financial time series. Our sequentially optimised radial basis function network (RBFNet) outperforms a random-walk baseline and several powerful supervised learners. Our RBFNets naturally measure the similarity between test samples and prototypes that capture the characteristics of the feature space. In chapter 5, Reinforcement learning for systematic FX trading, we perform feature representation transfer from an RBFNet to a direct, recurrent reinforcement learning (DRL) agent. Earlier academic work saw mixed results. We use better features, second-order optimisation methods and adapt our model parameters sequentially. As a result, our DRL agents cope better with statistical changes to the data distribution, achieving higher risk-adjusted returns than a funding and a momentum baseline. In chapter 6, The recurrent reinforcement learning crypto agent, we construct a digital assets trading agent that performs feature space representation transfer from an echo state network to a DRL agent. The agent learns to trade the XBTUSD perpetual swap contract on BitMEX. Our meta-model can process data as a stream and learn sequentially; this helps it cope with the nonstationary environment. In chapter 7, Sequential asset ranking in nonstationary time series, we create an online learning long/short portfolio selection algorithm that can detect the best and worst performing portfolio constituents that change over time; in particular, we successfully handle the higher transaction costs associated with using daily-sampled data, and achieve higher total and risk-adjusted returns than the long-only holding of the S&P 500 index with hindsight

A hybrid of bekk garch with neural network for modeling and forecasting time series

Gold prices change rapidly from time to time. The change is not only in the mean, but also in the variability of the series. The Malaysian Kijang Emas (MKE) is the official national bullion gold coin of Malaysia and it is high in demand. The purchase and resale prices of MKE are determined by the prevailing international gold market price. However, the value of Ringgit Malaysia (RM) that is used to purchase MKE is affected by United States (U.S.) dollar. Thus, the purpose of this study is to develop the best model for forecasting international gold prices, U.S. dollar index and MKE prices by investigating their co-movement. In an attempt to find the best model, fifteen years of data for MKE prices, international gold prices in U.S. dollar and U.S. dollar index were used. This study initially considered three standard methods namely bivariate generalized autoregressive conditional heteroskedasticity (GARCH), trivariate GARCH and multilayer feed-forward neural network (MFFNN). Bivariate and trivariate GARCH are from Baba-Engle-Kraft-Kroner (BEKK) GARCH. The current study further hybridized these methods to improve forecasting accuracy. Bivariate and trivariate GARCH were used to examine the relationship between gold prices and U.S. dollar. The trivariate GARCH was modified to develop GARCH-in-mean model due to the existence risk that was expected in the data. Analysis was done by using E-Views software. However, analysis using MFFNN model and hybridized models were carried out using MATLAB software. Analyses of performances were evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). The MAPE for all in and out sample forecasts were less than 1%. The lowest values of MAPE were 0.8% for gold prices and 0.2% for U.S. dollar index. These low values were produced by using trivariate GARCH-in-mean model that was developed by the current study either as a single or hybdridized model with MFFNN. MSE recorded the values when trivariate GARCH-in-mean model was hybridized with MFFNN using 15 hidden nodes

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Model for international trade of sawnwood using machine learning models

The study tests the potential of machine learning models to analyse and forecast global bilateral trade flows of soft sawnwood by countries. The empirical trade flow data including annual import and export quantities and prices of soft sawnwood from 2000 to 2014 is obtained from FAOStat. We compare forecasting results from three methods, which can be classified as machine learning models: support vector machines (SVM), Neural networks and Random forests that is an ensemble of decision trees. The significant changes in the global sawnwood markets in the North America, Asia and Western Europe after the financial and economic crises of 2008-2009 raise the question to update the modelling of trade flows. The information on the global trade flow developments are important for decision makers involved in strategic planning and forecasting at the European Union-, national- or industry level. The aim of this study is to test methods previously quite rarely applied in the forest sector market modelling, but which could be helpful for analysts to visualize and examine a large amount of bilateral trade data to have a view on ongoing changes and to assess next years’ developments. The results also support the Finnish Forest Sector Outlook Studies, which are published biannually by the Natural Resources Institute Finland (Luke). As an example we present empirical results for Finland and for some of its main competitor countries and export destinations.201

A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features

In the realm of Intelligent Transportation Systems (ITS), accurate traffic speed prediction plays an important role in traffic control and management. The study on the prediction of traffic speed has attracted considerable attention from many researchers in this field in the past three decades. In recent years, deep learning-based methods have demonstrated their competitiveness to the time series analysis which is an essential part of traffic prediction. These methods can efficiently capture the complex spatial dependency on road networks and non-linear traffic conditions. We have adopted the convolutional neural network-based deep learning approach to traffic speed prediction in our setting, based on its capability of handling multi-dimensional data efficiently. In practice,the traffic data may not be recorded with a regular interval, due to many factors, like power failure, transmission errors,etc.,that could have an impact on the data collection. Given that some part of our dataset contains a large amount of missing values, we study the effectiveness of a multi-view approach to imputing the missing values so that various prediction models can apply. Experimental results showed that the performance of the traffic speed prediction model improved significantly after imputing the missing values with a multi-view approach, where the missing ratio is up to 50%