Sharif Digital Repository

Permutation approach is suggested as a method to investigate financial time series in micro scales. The method is used to see how high frequency trading in recent years has affected the micro patterns which may be seen in financial time series. Tick to tick exchange rates are considered as examples. It is seen that variety of patterns evolve through time; and that the scale over which the target markets have no dominant patterns, have decreased steadily over time with the emergence of higher frequency trading  

Synchronization behavior of electroencephalographic (EEG) signals is important for decoding information processing in the human brain. Modern multichannel EEG allows a transition from traditional measurements of synchronization in pairs of EEG signals to whole-brain synchronization maps. The latter can be based on bivariate measures (BM) via averaging over pair-wise values or, alternatively, on multivariate measures (MM), which directly ascribe a single value to the synchronization in a group. In order to compare BM versus MM, we applied nine different estimators to simulated multivariate time series with known parameters and to real EEGs. We found widespread correlations between BM and MM,... 

Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy-novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a... 

Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to... 

This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error back-propagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time... 

Ubiquitous power-law as a fingerprint of Self-Organized Criticality (SOC) is used for describing catastrophic events in different fields. In this paper, by investigating the prerequisites of SOC, we show that SOC-like dynamics drive a correlation among disturbances in Iranian bulk power systems. The existence of power-law regions in probability distribution is discussed for empirical data using maximum likelihood estimation. To verify the results, long time correlation is evaluated in terms of Hurst exponents, by means of statistical analysis of time series, including Rescaled Range (R/S) and Scaled Windowed Variance (SWV) analysis. Also, sensitivity analysis showed that for correct... 

Based on series of crude oil rheological properties and asphaltene flocculation confocal microscopy analysis, Najafi et al., (2011) reported the existence of an optimum radiation time at which asphaltenic crude oils reach the minimum kinematic viscosity. Accordingly, they proposed the idea of asphaltene flocculation inhibition due to wave radiation.The present investigation is a continuous effort to provide more information about the process of flocculation inhibition. Confocal microscopy and rheological analyses are performed on different crude oils to prove the repeatability of the observed phenomena. The asphaltene content analysis was done based on IP143 procedure, which provides more... 

This paper presents a new method in order to predict the monthly electricity peak load of a country based on the prediction of Discrete Fourier Transform (DFT) of monthly peak electricity demand variation using the ARIMA methodology. For validation, the result of this method was used to predict monthly peak load variation of the recent two years in Iranian national grid. The primary goal of this article is to show the application and implementation of Discrete Fourier Transform to predict monthly variation of electricity peak load in national electric power systems. Furthermore, it is elaborated to demonstrate the benefits and shortcomings of DFT approach comparing to the commonly used... 

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all... 

The penetration of wind energy in power systems has been growing due to its interminable and mild environmental effects. The intrinsic attributes of this environmentally-friendly energy, i.e., the stochastic nature of wind farms generation, however, imposes various technical and financial challenges into power systems. So, developing an accurate wind farm modeling approach aimed at taking into account the wind generation intermittency can relieve many of these challenges. Therefore, this paper takes a step to an efficient wind farm modeling procedure employing an accurate as well as well-known Neural Network (NN)-based tool. The proposed approach is comprised of two main steps. The wind... 

We carry out a series of cross-correlation analysis of raw well-log data, in order to study the possible connection between natural gamma ray (GR) logs and other types of well logs, such as neutron porosity (NPHI), sonic transient time (denoted usually by DT), and bulk density (RHOB) of oil and gas reservoirs. Three distinct, but complementary, methods are used to analyze the cross correlations, namely, the multifractal detrended cross-correlation analysis (MF-DXA), the so-called Qcc(m) test in conjunction with the statistical test-the χ2(m) distribution-and the cross-wavelet transform (XWT) and wavelet coherency. The Qcc(m) test and MF-DXA are used to identify and quantify the strength of... 

Water quality is one of the most important factors contributing to a healthy life. From the water quality management point of view, TDS (total dissolved solids) is the most important factor and many water developing plans have been implemented in recognition of this factor. However, these plans have not been perfect and very successful in overcoming the poor water quality problem, so there are a good volume of related studies in the literature. We study TDS and the water flow of the Karoun river in southwest Iran. We collected the necessary time series data from the Harmaleh station located in the river. We present two Univariate Seasonal Autoregressive Integrated Movement Average (ARIMA)... 

Of the various conditions that affect space weather, Sun-driven phenomena are the most dominant. Cyclic solar activity has a significant effect on the Earth, its climate, satellites, and space missions. In recent years, space weather hazards have become a major area of investigation, especially due to the advent of satellite technology. As such, the design of reliable alerting and warning systems is of utmost importance, and international collaboration is needed to develop accurate short-term and long-term prediction methodologies. Several methods have been proposed and implemented for the prediction of solar and geomagnetic activity indices, but problems in predicting the exact time and... 

In this study, two conceptual models, the classic reservoir (CR) model and exchange reservoirs model embedded by dual porosity approach (DPR) are developed for simulation of karst aquifer functioning drained by multiple outlets. The performances of two developed models are demonstrated at a less developed karstic aquifer with three spring outlets located in Zagros Mountain in the south-west of Iran using 22-years of daily data. During the surface recharge, a production function based on water mass balance is implemented for computing the time series of surface recharge to the karst formations. The efficiency of both models has been assessed for simulation of daily spring discharge during the...