El desarrollo de gran parte de los modelos y métodos estadísticos, específicamente relacionados con series temporales, ha ido ligado al deseo de estudiar aplicaciones específicas dentro de diversos ámbitos científicos. El presente trabajo también surgió con el objetivo de resolver diversos problemas que se plantean dentro del ámbito econométrico, aunque también puede ser usado en otros ámbitos, todos ellos ligados con un conjunto de datos históricos y con una aplicación muy concreta al estudio del “egreso de divisas” en Bolivia. Se han estudiado a profundidad los modelos para series temporales que únicamente dependían del pasado de la propia serie. En el presente trabajo se inicia el análisis de una serie temporal teniendo en cuenta algún tipo de información externa. En el capítulo 1 se sustenta fuertemente el hecho de investigar acerca de aspectos ajenos a la serie temporal que llegan de algún modo a alterar su normal comportamiento. El capítulo 2 desarrolla minuciosamente modelos univariantes conocidos con el nombre de ARIMA, desarrollando su parte teórica. Posteriormente se complementa esta perspectiva univariante añadiéndose una parte determinística correspondiente al análisis de intervención construyendo así el modelo ARIMA CON INTERVENCIONES, la utilización de éstos modelos es comparada en el capítulo 3, de esta manera se distingui cual de los dos es más efectivo cuando los datos son afectados por eventos circunstanciales. La metodología del modelo ARIMA CON INTERVENCIONES es una herramienta útil para “modelizar” el comportamiento de las series temporales que presentan modificaciones a raíz de eventos ajenos que no pueden ser controlados.

SVAR ,VECM and ARIMA model for forecasting exchange rate SVAR model has a better performance

Mise en oeuvre de techniques de modélisation récentes pour la prévision statistique et économique.

Petrochemicals industry is one of the major industries in modern economy. Almost all synthetic chemicals or materials are related. Ethylene is the main product of this industry, which is produced from naphtha cracker. Butadiene is a by-product of naphtha cracking, but it is the most important raw material of synthetic rubbers. Most synthetic rubber plants do not have their own butadiene plant. All the materials were supplied from up-stream crackers. Due to the unstable supply and price surge recently, the cost of butadiene is higher than 70% of the total production cost in synthetic rubbers manufacturing. Huge price fluctuation in butadiene caused a big financial loss in 2008. These economic situations have made the price prediction an important issue for synthetic rubber manufacturers. In this research, we reviewed the historical price data of crude oil, naphtha, ethylene, propylene, butadiene and MTBE from the upper stream of petrochemicals in supply and the price of nature rubber, synthetic rubbers in demand. This study intends to find the petrochemicals price variation factors and uses butadiene as example. ¡§Time series¡¨ and OLS methods are used to build price prediction models to explain the price fluctuation in the past. Our research has found that the monthly price of butadiene is highly related to last month¡¦s price of butadiene, crude oil price and even related to last 9 month butadiene price. This may be because of the regular economical circulation and naphtha crackers annual turn around. The price of spot trading is related to the last week¡¦s price of butadiene and its co-product propylene. Those findings are valuable for synthetic rubber manufacturer in raw material procurement.

Líbalová, M., Utilization of value and growth strategies while investing in the US market. Diploma thesis. Brno: Mendel University in Brno, 2015. This thesis deals with the comparison value and growth investment strategy. There are observed the development of indicators P/E, P/S, P/BV, under which are the shares placed in individual strategies. The thesis also lists the indicators recommended by Benjamin Graham to observe, when investors making an in-vestment decision. There is created a predictive model that predict the devel-opment value funds applying these strategies. It also explores what is the correlation between individual issues of shares and entire portfolio, and how this fact affects the development of the course.

Critical equipment plays an essential role in industry because of its lack of redundancy. Failure of critical equipment results in a major economic burden that will affect the profit of the enterprise. Lack of redundancy for critical equipment occurs because of the high cost of the equipment usually combined with its high reliability. When we are analyzing the reliability of such equipment, as a result, there are few opportunities to crash a few pieces of equipment to actually verify component life. Reliability is the probability that an item can perform its intended function for a specified interval of time under stated conditions and achieve low long-term cost of ownership for the system considering cost alternatives. From the economical standpoint, the overriding reliability issue is cost, particularly the cost of unreliability of existing equipment caused by failures. Classical questions about reliability are: · How long will the equipment function before failure occurs? · What are the chances that a failure will occur in a specified interval for turnaround? · What is the best turnaround interval? · What is the inherent reliability of the equipment? · What are the risks of delaying repair/replacements? · What is the cost of unreliability? · … We will try to answer these questions for a critical reciprocating compressor, which has been in service for only 4 years and has undergone only few failures. Professionals in all industries are faced with the problems of performing maintenance actions and optimizing maintenance planning for their repairable systems. Constructing stochastic models of their repairable systems and using these models to optimize maintenance strategies require a basic understanding of several key reliability and maintainability concepts and a mathematical modeling approach. Therefore, our objective is to present fundamental concepts and modeling approaches in the case of a critical reciprocating compressor. We developed a stochastic model not to simulate a reciprocating compressor with a complete set of components but mainly to optimize the overhaul period taking into account the main failure modes only. How to lower the cost? How to reduce or remove maintenance actions that are not strictly necessary? How to improve the long-term profitability of ageing plants with the strict respect of Health-Safety-Environment HSE requirements? A reciprocating compressor is a complex machine that cannot be described with a single reliability function. A compressor has several failure modes. Each failure mode is assumed to have its own Weibull cumulative distribution function. The compressor is then a system with several Weibull laws in series. We will extend the usual procedure for minimizing the expected total cost to a group of components. Different components may have different preventive maintenance “needs”, but optimizing preventive maintenance at the component level may be sub-optimal at the system level. We will study also the reliability importance indices that are valuable in establishing direction and prioritization of actions related to a reliability improvement plan, i.e. which component should be improved to increase the overall lifetime and thus reduce the system costs. When considering a large system with many items that are maintained or replaced preventively, it is advantageous to schedule the preventive maintenance in a block such that the system downtime is kept as small as possible. This requires that the resources are available so that the maintenance of components can be performed simultaneously or according to a well-defined sequence. The result of the stochastic model optimization came as a surprise. We thought to find a new mean-time-between-failure MTBF, larger than the actual overhaul period. Actually, the model showed that there is no economical interest to schedule a systematic preventive maintenance for this reciprocating compressor. Nevertheless, we cannot wait for a failure (and the associated corrective maintenance) because the loss-of-production cost is too high and this compressor has no spare. Preventive maintenance is not the optimum strategy, but predictive maintenance is. But what means predictive maintenance? It is a maintenance policy to regularly inspect equipment to detect incipient changes or deterioration in its mechanical or electrical condition and performance. The idea behind this is to perform corrective maintenance only when needed, before the occurrence of failure. We need to find how to detect performance deterioration of the compressor with a couple of weeks or days notice before failure. So it is possible to schedule a right maintenance activity at the optimum moment. To summarize, the main findings of this thesis are · a new method to estimate the shape factor of a Weibull distribution function, · a stochastic model demonstrating that we have to move from systematic preventive maintenance to predictive maintenance, · a low cost system based on thermodynamic approach to monitor a reciprocating compressor, · an automatic detection of performance deterioration.

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In this paper, we briefly describe the automatic model selection which is provided by Autometrics in the PcGive program. The modeler only needs to specify the initial model and the significance level at which to reduce the model. Then, the algorithm does the rest. The properties of Autometrics are discussed. We also explain its background concepts and try to see whether the model selected by the Autometrics can perform well. For a given data set, we use Autometrics to find a “new” model, and then compare the “new” model with a previously selected one by another modeler. It is an interesting issue to see whether Autometrics can also find models which fit better to the given data. As an illustration, we choose three examples. It is true that Autometrics is labor saving and always gives us a parsimonious model. It is really an invaluable instrument for social science. But, we still need more examples to strongly support the idea that Autometrics can find a model which fits the data better, just a few examples in this paper is far from enough.

The objective of this thesis is to study the load forecasting of distribution feeders and substations for Fong-Shan District of Taiwan Power Company. To increase the accuracy of load forecasting, the load characterization of customers served has been investigated. The typical load patterns of different customers classes and derived by performing the statistic of power consumption data retrieved. The daily load profiles and load consumptions data distribution feeders and substations have been solved by considering the typical load patterns and energy consumption of all customers served. To investigate the correlation ship of temperature and energy consumption of customer classes, the temperature sensitivity of customer energy consumption has been used to update the load composition and the contribution of load change by different customer classes. To perform the load forecasting of distribution systems, the linear, nonlinear and hybrid load forecasting modules have been proposed. The historical load data of distribution feeders and substations in Fong-Shan District have been used to derive the load forecasting modules. To analyze the accuracy of load forecasting by considering the temperature effect, the temperature change is included in the load forecasting module. With the load forecasting derived, the proper load transfers among different distribution feeders and different substations have been determined to achieve the load balancing of service areas.

This thesis presents a comparison for modeling and forecasting Chinese futures market of copper and aluminum with single time series and multivariate time series under linear restrictions. For single time series, data transformation for stationary purpose has been tested and performed before ARIMA model was built. For multivariate time series, co-integration rank test has been performed and included before VECM model was built. Based on selected models, the forecasting shows multivariate time series analysis has a better result than single time series, which indicates utilizing the relationships among the series can improve the accuracy of time series forecasting.