Sharif Digital Repository

The seasonal fluctuations in economics variables relate to the different behavior of economic agents across different seasons. In past, seasonality has been viewed as a redundant feature that needs to be removed from data before economic analysis. From 1988, modeling seasonality has become the major concern of many economists; moreover, it was seen that many economic analysis and forecasts could be flawed if seasonality is ignored. In the present research, periodic times series approach is used for the first time in modeling the seasonality feature of the housing market. Regarding the importance of the housing sector in economy from micro and macroeconomic points of view, using a more... 

Since recording first electroencephalogram (EEG) of human brain in 1929 until now it becomes as a powerful tool in neuroscience. At first information extraction was done by visionary approaches only. But because of some problems in the context of inaccuracy and also in analyzing data different methods were proposed in order to extract hidden information of EEG. Among these approaches Fourier transformation was suggested as a very useful method that could draw out so many characteristics of signal different frequency components. However this way had many faults that cause limitation in analyzing time series and as a result other methods have been considered. One method that later has been... 

Translocation processes are ubiquitous in biology and biotechnology. Translocation of small molecules, e. g. sugar from maltoporin, metabolites through bacteria and macromolecules like proteins, from channels of cellular organelles and or RNA translocation though
nuclear pores are of vital importance for cellular metabolism. One of the important applications of translocation processes in biotechnology is to sense translocating macromolecules or small molecules by analyzing the current passing through natural or synthesis channels. Improving our knowledge about this process can also help us to develop new methods for designing the appropriate drugs. In this thesis by studying and... 

Tehran possesses various environmental crises due to excessive population growth, a huge increase of vehicles and heavy concentrated industries. One of the most important concern is air pollution. Spectral Analysis by discrete Fourier transform are described and applied to harmonic analysis of time series for detecting Present periodicities.
The current work proposes an approach for the determine the contribution of different frequencies to the data variance using air quality measured data. In this research, we present a comprehensive review of methods for spectral analysis of nonuniformly sampled data. Because of The air quality data in Tehran have irregular sampling periods and... 

In this thesis we are going to forecast the hourly consumption of the electricity over the country with two models and then, combine them. The first model decomposes the consumption to a deterministic trend and a stochastic residual. The second one assumes that the trend part is also stochastic.Once the consumption is being predicted separately by the models, in the second part of the thesis, we will combine the results to get a final prediction. This prediction is going to be compared with the load forecast of the Dispatching Unit of the electricity network as a base model. We are going to answer two important questions: firstly, does combining the models give a better prediction or not,... 

An important class of applications which have been rapidly growing recently is the one that create and use time series data. These types of data sets are ordered based on the timestamps associated to their data items. In practice, traditional relational databases are unable to satisfy the requirements of these data sets; however, NoSQL databases with column-wide data structure are appropriate infrastructure for them. These databases are very efficient in read and write operations (especially for time series data, which are ordered) and are able to store unstructured data. Time series data may contain valuable and sensitive information; hence, they should be protected from the information... 

Financial and monetary markets are appropriate areas of applying Chaos Theory. Firstly, current theories of financial and monetary economics state that economic and financial variables such as exchange rates and stock prices are stochastic, so forecasting them is almost impossible. Secondly, if we find the hidden ordered and deterministic trends, we can achieve considerable profits. In this piece of research, we evaluate different methods and tests of detecting chaos in financial time series, and choose the most applicable methods to test financial markets’ indices. The main three indices of Tehran Stock Exchange, including Price, Finance and Industry indices, are examined. A sample of the... 

In neural systems of living organism, moreover than differences in anatomic structure of cells, there is also differences in physiological functions of analogous cells.Specification and categorization of neurons based on physiological functions is one of objectives of neuroscience. Study of cognitive behaviors and systematic study of neural system, modeling and practical applications in neural prosthesis design are some of applications of categorizing neural cells. Neural signals can be studied by Spike rate of a single neuron activity or Local Field Potential (LFP) of a finite number of neurons. In previous studies neurons of first visual cortex are divided into two groups of simple and... 

In the field of supply chain, management constantly seeks to adopt strategies for realizing its targeted objectives. The supply chain, therefore, includes integrated production-distribution processes with all effective parts where proper management requires a comprehensive and dynamic approach. It is necessary to meet required items at required time. This becomes more important with the supply systems providing items. On the other hand, the difficulty of supply chain management, decisions, and policy-making can be increased by a wide range of conditions such as changes in demand. Thus, a comprehensive approach is needed to take the present dynamics into account. The current paper develops a... 

Human as an important part of the natural environment and can exert their positive or negative effects in the form of long-term and short-term on a natural environment. Human impacts on the natural system are very complex and consist of various components. The most considerable among them from past to now are maybe land use and land cover change, although, the impact of dam construction, water pollution, air pollution and etc, can not be neglected. To quantify the impact of these changes, many researchers have studied meteorological and climatic parameters using statistical relationships, but one major problem always existed, the low spatial accuracy of meteorological data. In recent years,... 

Support vector regression (SVR) in the field of machine learning attracted much attention because of its attractive features and high efficiency for high-dimensional and nonlinear data. Although support vector regression has shown to be very effective for prediction problems, it is necessary to adjust the parameters contained therein to obtain the desired output with error rates. In the past, this was done manually, by trial and error. Over time and by development of optimization algorithms, one of the newest methods to solve such problems is the meta-heuristic optimization algorithms. Therefore, in this thesis, we use the firefly optimization algorithm, which is a population-based... 

One of the most surprising lessons of dynamical systems theory is that the phase spaces of simple nonlinear systems may contain strange attractors. For certain simple systems, such as pendulum, direct observations of the phase space and finding strange attractors are possible. But physical systems in which all the relevant dynamical variables can be simultaneously monitored are relatively scarce. Takens’ theory shows how a time-series of measurements of a single observable state can be often used to reconstruct qualitative features of the phase space of the system. In this research, using the time-series of one measurable state, an algorithm is proposed to control chaos. The approach... 

Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. To obtain these goals the analysis of fMRI is the first condition which should be met. First methods were linear and assumed the... 

Studies in recent years have shown formation of strongly functionally linked sub-networks during rest, networks that are often referred to as resting-state networks. RSNs not only have basic information about the brain but also play a key role in detecting brain disorders, such as Alzheimer and Autism; Consequently, they have been remarkably noticed by neuroscientists. Numerous methods have been used in order to extract RSNs using resting-states fMRI time series. Independent component analysis (ICA) is the most common method, whi have been reported to show a high level of consistency neurophysiology; however, its results is unstable in subject-level. is weakness restricted the ICA... 

Chaotic time series analysis, study of statistical behavior of chaotic maps and eventually an attempt to reconstruction and prediction of dynamical and statistical properties of output data of chaotic systems using stochastic models such as Markov models and autoregressive-moving average models are the main purposes of the present research. Examples of chaotic time series abound in the output of economics, engineering systems, the natural sciences (especially geophysics and meteorology) and social sciences. An intrinsic feature of an output time series of a dynamic system is that, adjacent observations are dependent. Time series analysis is concerned with techniques for the analysis of this... 

This project tries to select the inputs which really affect the change in the direction of S&P500. For this purpose, design of experiments and analysis of variance are used. T tests are carried out to calculate the statistical significance of mean differences. Experiment results indicate that the designed neural networks with the selected inputs significantly outperform the traditional logit model with respect of the number of correct predictions. Moreover, real trades are simulated using the neural network predictions in the test period and the results show that using the designed neural network can significantly increase the income.

 

Knowing the time of change would narrow the search to find and identify the variables disturbing a process. Having this information, an appropriate corrective action could be implemented and valuable time could be saved. Multistage processes that are often observed in current manufacturing processes must be monitored to assure quality products. The change-point detection of such processes has not been proposes investigated yet. Thus, this dissertation proposes maximum likelihood step-change estimators of two kinds of these processes. First, a multistage process with variable quality characteristics is considered and formulated by the first-order auto-regressive model. For the location... 

In today’s competitive world, using an efficient forecast method is necessity for companies. To now, many forecast methods have been developed, but many of them have not an expected efficiency. In this research, we develop a new hybrid forecast method with application of forecasting retails demand. The hybrid method is the combination of ARIMA method and neural networks. To test the efficiency of the method we use the 96 weeks data of plastic containers demand. We also comprise the hybrid method with other forecast methods including naïve method, ARIMA method and neural network method by applying root mean square error and mean absolute percentage error indexes. In the case of plastic... 

Failure of mechanical parts in the industry lead to a larger system downtime and even imposing economic losses to the factory. For this Purpose, for many years, researchers have been trying to find ways to predict early failure and to prevent losses from occurring. Creation of new sciences like artificial intelligence, helped researchers in this field.In the current study, using experimental data of a set of bearings that have been tested and recorded in the Intelligent Systems Research Center, A new approach with sufficient accuracy is presented for the prediction algorithm. Among the features extracted, three features of entropy, root mean square and maximum are the most appropriate... 

P/E ratio is studied in four levels in this study:
1)Macroeconomics level
2)Capital market level
3)Industry level
4)Company level
The first level studies effects of macroeconomics variables on P/E ratio. At this level we use variables such as economic growth, inflation, exchange rate, and etc.The next level uses capital market variables such as market volume, and IPO information.The third level that we study in this research is industry level. Stocks of an industry generally behave similar, because they have common advantages and disadvantages, thus industry is an effective factor on P/E ratio.The last level studies financial statements and internal features of a...