Pasaulio ekonomika vyksta ekonomikos ciklais, po kiekvieno pakilimo seka nuosmukis, o po nuosmukio – pakilimas. Keičiantis ekonominio ciklo fazėms, skiriasi šalių priimami sprendimai, vyriausybių vykdomos politikos, priemonės, tikslai. Tyrimo objektas – finansinių krizių poveikis ekonomikai, darbo tikslas – išanalizuoti finansinių krizių poveikį ekonomikai ir sukurti ekonomikos atsigavimo po krizės prognozavimo sistemą. Šiam tikslui pasiekti iškelti šie svarbiausi uždaviniai: ištirti pagrindines priežastis, dėl kurių kilo pasaulinės krizės, išanalizuoti pagrindines priemones, kurių buvo imtasi, norint sumažinti finansinių krizių poveikį ekonomikai, išnagrinėti ir apibendrinti užsienyje atliktų tyrimų apie finansines krizes rezultatus, atlikti ekonomikos atsigavimo po finansinės krizės prognozę Lietuvoje ir palyginti su pasaulio atsigavimu po pasaulinės finansinė krizės. Darbe pateikiamos pagrindinės finansinės ekonominės krizės nuo 1636 metų. Jos skirstomos pagal savo pradinę ar pagrindinę priežastį. Pateikiamas finansinių krizių poveikis ekonomikai bei priemonės, kurių imasi skirtingos valdžios, kad padėtų šalims greičiau atsigauti po patirtų nuosmukių. Nors teoriškai yra nesunku suvokti atitinkamas priemones, kurių reikėtų imtis užklupus ekonominiam nuosmukiui, tačiau tos pačios priemonės skirtingai reaguoja atitinkamomis aplinkybėmis ir atskirais laikotarpiais, todėl gali neduoti laukiamų ar tikėtinų rezultatų. Atlikta prognozė dažniausiai su 1-3 % paklaida atitiko... [toliau žr. visą tekstą] / The global economics goes cycle after cycle. After every boom follow recession, after every recession follow boom. Governments made different sollutions of their politics for every cycle of economics. The subject of research is the impact of financial crises on economics, the objective of the work – to sift the impact of financial crises on economics and to make the system of recovery of economics. In order to achieve this goal, are set these basics tasks: to analyse basic consequences of financial crises, to analyze basic means, which were made to recover economics, to analyze results of a few researchs, to make a forecast of recovery of economics in Lithuania and to compare with the recovery of the world. In this paper are presented basic financial crises from 1636. These financial crises are allocated related to their main reason. There are written the impact of financial crises and means, which were made by governments to help for countries and people. In theory, it is simple to know all the best means for economics, but it is difficult to practise them in specific country and situation. All means can affect economics not like it is necessary. The forecast confirm actual values with error of 1-3%. Because of this, the conclusion is made, that predictive values in 2010 happened in the world and in Lithuania too. The paper consists of three parts. The size of this paper is 45 pages. There are 6 tables and 14 pictures in this paper. 52 sources of literary are used.

The purpose of this bachelor thesis is to investigate different outcomes of the usage of photovoltaic (PV) power for electric vehicle (EV) charging adjacent to workplaces. In the investigated case, EV charging stations are assumed to be connected to photovoltaic systems as well as the electricity grid. The model used to simulate different scenarios is based on a goal of achieving constant power exchange with the grid by adjusting EV charging to a solar irradiance forecast. The model is implemented in MATLAB. This enables multiple simulations for varying input parameters. Data on solar irradiance are used to simulate the expected PV power generation. Data on driving distances are used to simulate hourly electricity demands of the EVs at the charging stations. A sensitivity analysis, based on PV irradiance that deviates from the forecast, is carried out. The results show what power the grid needs to have installed capacity for if no PV power system is installed. Furthermore, appropriate PV power installation sizes are suggested. The suggestions depend on whether the aim is to achieve 100 percent self-consumption of PV generated power or full PV power coverage of charging demands. For different scenarios, PV power installations appropriate for reducing peak powers on the grid are suggested. The sensitivity analysis highlights deviations caused by interference in solar irradiance.

This two part analysis looks at forecasting models in the United States' 2016 presidential election and breaks down the elections fundamental and contingency factors. This paper argues that political science forecasting models could be improved through a more localized approach and by utilizing additional contingency factors. The psychology study of this analysis explores the already established relationship between political conservatism and favorability ratings, as well as the relationship between perceived similarity between voter personality and candidate personality, referred to as personality mirroring, and favorability ratings. The study uses past research to suggest that these relationships for the 2016 presidential candidates, Hillary Clinton and Donald Trump, can be explained through mediating variables: leader effectiveness and trust. The study used participants recruited through Amazon’s Mechanical Turk for data, all adults who voted in the 2016 US presidential election. The study found that there was a full mediation of leadership effectiveness for Donald Trump and significant partial mediation for the other three explored relationships.

Parameter selection is a critical task in scientific workflows in order to maintain the accuracy of the simulation in an environment where physical conditions change dynamically such as in the case of weather research and forecast simulations. Currently, Numerical Weather Prediction (NWP) is the premier method for weather prediction, which is used by the National Oceanic and Atmospheric Administration (NOAA). It takes the current observations from observed sites as the input for numeric computer models and then produces the final prediction. Considering the large number of simulation parameters, the size of the configuration search space becomes prohibitive for rapidly evaluating and identifying the parameter configuration that leads to most accurate prediction. In this thesis, we develop an Autonomic Workflow Performance Manager (AWPM) for Hurricane Integrated Modeling System (HIMS). AWPM is implemented on top of the Apache Storm and ZooKeeper to handle multiple real-time data streams for weather forecast. AWPM can automatically manage model initialization and execution workflow and achieve better performance and efficiency. In our experiments, AWPM achieves better performance and efficiency for the model initialization and execution processes, by utilizing automatic computing, distributed computing and component-based development. We reduced the timescale of the configuration search workflow by a factor of 10 by using 20 threads with the full search method, and a factor of 20 by with the roofline method when compared to serial workflow execution as it is typically performed by domain scientists.

This study was issued by Swedbank because they wanted too improve their GDP growth forecast capabilites.  A program was developed and tested on six countries; USA, Sweden, Germany, UK, Brazil and Norway. In this paper I investigate if I can reduce forecasting error for GDP growth by taking a smart average from a variety of models compared to both the best individual models and a random walk. I combine the forecasts from four model groups: Vector autoregression, principal component analysis, machine learning and random walk. The smart average is given by a system that give more weight to the predictions of models with a lower historical error. Different weighting schemas are explored; how far into the past should we look? How much should bad performance be punished? I show that for the six countries studied the smart average outperforms the single best model and that for five out of six countries it beats a random walk by at least 25%. / Den här studien beställdes av Swedbank eftersom de ville förbättra sin BNP-prediktionsförmåga. Ett dataprogram utvecklades och testades på sex länder; USA, Sverige, Tyskland, Storbritannien, Brasilien och Norge. I den här rapporten undersöker jag om jag kan minska felmarginalen för BNP-utvecklingsprognoser genom att ta ett smart genomsnitt från flera olika modeller jämfört med både den bästa individuella modellen och en random walk. Jag kombinerar prognoser från fyra modellgrupper: Vektor autoregression, principalkomponentanalys, maskininlärning och random walk. Det smarta genomsnittet skapas genom att ge mer vikt till de modeller som har lägst historiskt felmarginal. Olika viktningsscheman utforskas; hur långt bak i tiden ska vi mäta? Hur hårt ska dåliga prediktioner bestraffas? Jag visar att för de sex länderna i studien presterar det smarta genomsnittet bättre än den enskilt bästa modellen och fem av de sex länderna slår en random walk med mer än 25%.

Corporate investment decisions are among the most important decisions of a firm. Internal capital markets play a key role in facilitating the allocation of capital resources in order to finance investment projects within diversified firms. This thesis investigates internal capital markets and its relationship with analysts earnings forecast errors in three countries with two distinct financial systems, namely, the market-based and bank-based financial system. Using segment level data for public listed companies in the UK, France and Germany between 2005 and 2010, we examine the operation and efficiency of internal capital markets in market- and bank-based systems. We also examine the impact of the financial crisis of 2008 on internal capital markets and analysts earnings forecasts errors, namely, the accuracy, bias and dispersion. The findings indicate internal capital markets actively facilitate the allocation of resources within diversified firms and, in general, operate inefficiently. Furthermore, internal capital markets appear to be more active in France compared with the UK. On the other hand, their role appears to be limited in Germany, as segments appear to rely more on their own resources and less on internal capital markets for investments. In addition, we find that internal capital market activity declines and efficiency improves during the financial crisis in UK. In contrast, there is no significant evidence to suggest that efficiency improves during the crisis in France or Germany. This research also finds some evidence to suggest internal capital markets operations aggravate firm complexity and, in turn, negatively affect short-term forecast accuracy in the UK. In addition to this, our analysis shows there is a positive relationship between the size of internal capital markets and dispersion in analysts earnings forecasts. In general, our study shows analysts are optimistic about firms future performance; however, the level of optimism significantly declines during the financial crisis. Lastly, we report a positive relationship between efficiency of internal capital markets and optimism in earnings forecasts.

This thesis provides a basic framework for probabilistic real-time urban flood forecasting based on data of varying degree of quality and quantity. The framework was developed based on precipitation data from two case study areas:Aarhus Denmark and Castries St. Lucia. Many practitioners have acknowledged that a combination of structural and non-structural measures are required to reduce the effects of flooding on urban environments, but the general dearth of the desired data and models makes the development of a flood forecasting system seem unattainable. Needless to say, high resolution data and models are not always achievable and it may be necessary to override accuracy in order to reduce flood risk in urban areas and focus on estimating and communicating the uncertainty in the available resource. Thus, in order to develop a pertinent framework, both primary and secondary data sources were used to discover the current practices and to identify relevant data sources. Results from an online survey revealed that we currently have the resources to make a flood forecast and also pointed to potential open source quantitative precipitation forecast (QPF) which is the single most important component in order to make a flood forecast. The design of a flood forecasting system entails the consideration of several factors, thus the framework provides an overview of the considerations and provides a description of the proposed methods that apply specifically to each component. In particular, this thesis focuses extensively on the verification of QPF and QPE from NWP weather radar and highlights a method for estimating the uncertainty in the QPF from NWP models based on a retrospective comparison of observed and forecasted rainfall in the form of probability distributions. The results from the application of the uncertainty model suggest that the rainfall forecasts has a large contribution to the uncertainty in the flood forecast and applying a method which bias corrects and estimates confidence levels in the forecast looks promising for real-time flood forecasting. This work also describes a method used to generate rainfall ensembles based on a catalogue of observed rain events at suitable temporal scales. Results from model calibration and validation highlights the invaluable potential in using images extracted from social network sites for model calibration and validation. This framework provides innovative possibilities for real-time urban flood forecasting.

The main aim of the thesis is to estimate a needed capacity of nursing homes and houses with home help services in the district Nový Jičín. The year 2030 was chosen as a time horizon. Current facilities, age and sex structure of their users in this district were described by means of field inquiry. The proportion of users of facilities was estimated from real population of the district at specific age. Important part of the thesis is a forecast of population growth of the district, which was based on the previous population development there. This trend was compared with population development in Moravian-Silesian region and in the Czech Republic as a whole in the special chapter. The estimate of needed capacity of above-mentioned facilities connects the forecast of the future development of age structure of elderly with contemporary availability of the facilities providing care for elderly. The thesis also describes the development of facilities providing care for elderly in the Czech Republic, furthermore it analyses the factors that can influence demand for these facilities.

Analysis and forecast of the number and demographic structure of dentists in the Czech Republic Abstract Demographic aging and the associated fear of shortage of dentists in the near future is currently frequent topic. This problem is caused in the late seventies and early eighties, when unusually large number of graduates of dental disciplines finished studium and became dentists. In the next few years, the number of graduates declined significantly, and then held for decades at a constant level. This dominant age category of dentists has currently reached retirement age. A large number of old dentists provoked a reaction in the form of re-increase university capacity and current number of graduates is comparable with numbers in the period before 35-40 years ago. The purpose of this paper is to examine this changes and decide, if this changes were sufficient to keep current situation in the field of dental health care. Keywords: dentists, aging of dentists, forecast, age structure, Czech republic

This Master's Thesis describes the evolution of the Internet, its importance in marketing and new trends and tools used in internet marketing. It also deals with question of potential Internet collapse in future, describes variants of how it could happen and gives the suggestions to solve this problem, too. The Master's Thesis also includes forecasts of evolution of the Internet till 2030 and forecasts of major global agencies concerning the evolution of information technologies and technological news, which can significantly influence the entrepreneurial activity of many companies all over the world.