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    Permutation approach, high frequency trading and variety of micro patterns in financial time series

    , Article Physica A: Statistical Mechanics and its Applications ; Vol. 413, issue , 2014 , pp. 25-30 ; ISSN: 03784371 Aghamohammadi, C ; Ebrahimian, M ; Tahmooresi H ; Sharif University of Technology
    Abstract
    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 of EEG: Bivariate and multivariate measures

    , Article IEEE Transactions on Neural Systems and Rehabilitation Engineering ; Vol. 22, Issue. 2 , 2014 , pp. 212-221 ; ISSN: 1534-4320 Jalili, M ; Barzegaran, E ; Knyazeva, M. G ; Sharif University of Technology
    Abstract
    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,... 

    Accurate and novel recommendations: an algorithm based on popularity forecasting

    , Article ACM Transactions on Intelligent Systems and Technology ; Vol. 5, issue. 4 , 2015 Javari, A ; Jalili, M ; Sharif University of Technology
    Abstract
    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... 

    Predicting oil price movements: A dynamic Artificial Neural Network approach

    , Article Energy Policy ; Vol. 68, issue , 2014 , p. 371-382 Godarzi, A. A ; Amiri, R. M ; Talaei, A ; Jamasb, T ; Sharif University of Technology
    Abstract
    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... 

    Development of a robust identifier for NPPs transients combining ARIMA model and ebp algorithm

    , Article IEEE Transactions on Nuclear Science ; Vol. 61, issue. 4 , August , 2014 , p. 2383-2391 Moshkbar-Bakhshayesh, K ; Ghofrani, M. B ; Sharif University of Technology
    Abstract
    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... 

    Exploring self-organized criticality conditions in Iran bulk power system with disturbance times series

    , Article Scientia Iranica ; Vol. 21, issue. 6 , 2014 , p. 2264-2272 ; 10263098 Karimi, E ; Ebrahimi, A ; Fotuhi-Firuzabad, M ; Sharif University of Technology
    Abstract
    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... 

    Nonlinear dynamical structure of sway path during standing in patients with multiple sclerosis and in healthy controls is affected by changes in sensory input and cognitive load

    , Article Neuroscience Letters ; Volume 553 , 2013 , Pages 126-131 ; 03043940 (ISSN) Negahban, H ; Sanjari, M. A ; Mofateh, R ; Parnianpour, M ; Sharif University of Technology
    2013
    Abstract
    Although several studies have applied traditional linear measures to evaluate postural control of patients with multiple sclerosis (MS), little is known about the nonlinear dynamics of this patient group. In this study, recurrence quantification analysis (RQA), a well documented nonlinear method, was used to compare the nonlinear dynamical structure of postural sway in two groups consisting of MS patients (. n=. 23) and healthy matched controls (. n=. 23). The study focuses on three levels of postural difficulty consisting of (1) standing on a rigid surface (force platform) with eyes open, (2) standing on a rigid surface with eyes closed, and (3) standing on a foam surface with eyes closed.... 

    Estimating the parameters of globular cluster M 30 (NGC 7099) from time-series photometry

    , Article Astronomy and Astrophysics ; Volume 555 , 2013 ; 00046361 (ISSN) Kains, N ; Bramich, D. M ; Arellano Ferro, A ; Figuera Jaimes, R ; Jørgensen, U. G ; Giridhar, S ; Penny, M. T ; Alsubai, K. A ; Andersen, J. M ; Bozza, V ; Browne, P ; Burgdorf, M ; Calchi Novati, S ; Damerdji, Y ; Diehl, C ; Dodds, P ; Dominik, M ; Elyiv, A ; Fang, X. S ; Giannini, E ; Gu, S. H ; Hardis, S ; Harpsøe, K ; Hinse, T. C ; Hornstrup, A ; Hundertmark, M ; Jessen Hansen, J ; Juncher, D ; Kerins, E ; Kjeldsen, H ; Korhonen, H ; Liebig, C ; Lund, M. N ; Lundkvist, M ; Mancini, L ; Martin, R ; Mathiasen, M ; Rabus, M ; Rahvar, S ; Ricci, D ; Sahu, K ; Scarpetta, G ; Skottfelt, J ; Snodgrass, C ; Southworth, J ; Surdej, J ; Tregloan Reed, J ; Vilela, C ; Wertz, O ; Williams, A
    2013
    Abstract
    Aims. We present the analysis of 26 nights of V and I time-series observations from 2011 and 2012 of the globular cluster M 30 (NGC 7099). We used our data to search for variable stars in this cluster and refine the periods of known variables; we then used our variable star light curves to derive values for the cluster's parameters. Methods. We used difference image analysis to reduce our data to obtain high-precision light curves of variable stars. We then estimated the cluster parameters by performing a Fourier decomposition of the light curves of RR Lyrae stars for which a good period estimate was possible. We also derived an estimate for the age of the cluster by fitting theoretical... 

    Nonlinear seismic assessment of steel moment frames using time-history, incremental dynamic, and endurance time analysis methods

    , Article Scientia Iranica ; Volume 20, Issue 3 , 2013 , Pages 431-444 ; 10263098 (ISSN) Hariri Ardebili, M. A ; Zarringhalam, Y ; Estekanchi, H. E ; Yahyai, M ; Sharif University of Technology
    2013
    Abstract
    A recent method in the seismic assessment of structures is Endurance Time Analysis (ETA). ETA is a time-history-based dynamic pushover procedure, in which structures are subjected to gradually intensifying acceleration functions called Endurance Time Acceleration Functions (ETAFs), and their performances are evaluated based on the equivalent intensity level that they can endure while satisfying required performance goals. In this paper, the accuracy of the ETA in the seismic assessment of steel moment resisting frames is compared with the Time History Analysis (THA) and Incremental Dynamic Analysis (IDA) methods. For this purpose, a set of mid-rise and high-rise frames were selected as a... 

    Speech enhancement using hidden Markov models in Mel-frequency domain

    , Article Speech Communication ; Volume 55, Issue 2 , 2013 , Pages 205-220 ; 01676393 (ISSN) Veisi, H ; Sameti, H ; Sharif University of Technology
    2013
    Abstract
    Hidden Markov model (HMM)-based minimum mean square error speech enhancement method in Mel-frequency domain is focused on and a parallel cepstral and spectral (PCS) modeling is proposed. Both Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented for speech enhancement. To estimate clean speech waveform from a noisy signal, an inversion from the Mel-frequency domain to the spectral domain is required which introduces distortion artifacts in the spectrum estimation and the filtering. To reduce the corrupting effects of the inversion, the PCS modeling is proposed. This method performs concurrent modeling in both cepstral and magnitude spectral... 

    Multivariate Synchronization Analysis of Brain Electroencephalography Signals: A Review of Two Methods

    , Article Cognitive Computation ; Volume 7, Issue 1 , February , 2013 , Pages 3-10 ; 18669956 (ISSN) Jalili, M ; Sharif University of Technology
    Springer New York LLC  2013
    Abstract
    Temporal synchronization of neuronal activity plays an important role in various brain functions such as binding, cognition, information processing, and computation. Patients suffering from disorders such as Alzheimer’s disease or schizophrenia show abnormality in the synchronization patterns. Electroencephalography (EEG) is a cheap, non-invasive, and easy-to-use method with fine temporal resolution. Modern multichannel EEG data are increasingly being used in brain studies. Traditional approaches for identifying synchronous activity in EEG are through univariate techniques such as power spectral density or bivariate techniques such as coherence. In this paper, we review two methods for... 

    Early detection of apnea-bradycardia episodes in preterm infants based on coupled hidden Markov model

    , Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013 ; 2013 , Pages 243-248 Masoudi, S ; Montazeri, N ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Pladys, P ; Hernandez, A. I ; Sharif University of Technology
    IEEE Computer Society  2013
    Abstract
    The incidence of apnea-bradycardia episodes in preterm infants may lead to neurological disorders. Prediction and detection of these episodes are an important task in healthcare systems. In this paper, a coupled hidden Markov model (CHMM) based method is applied to detect apnea-bradycardia episodes. This model is evaluated and compared with two other methods based on hidden Markov model (HMM) and hidden semi-Markov model (HSMM). Evaluation and comparison are performed on a dataset of 233 apnea-bradycardia episodes which have been manually annotated. Observations are composed of RR-interval time series and QRS duration time series. The performance of each method was evaluated in terms of... 

    Properties of functional brain networks correlate frequency of psychogenic non-epileptic seizures

    , Article Frontiers in Human Neuroscience ; Issue DEC , 2012 ; 16625161 (ISSN) Barzegaran, E ; Joudaki, A ; Jalili, M ; Rossetti, A. O ; Frackowiak, R. S ; Knyazeva, M. G ; Sharif University of Technology
    Frontiers Media S. A  2012
    Abstract
    Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness metrics, respectively. Yet the number of PNES attacks per month correlated with a... 

    Estimating the step-change time of the location parameter in multistage processes using MLE

    , Article Quality and Reliability Engineering International ; Volume 28, Issue 8 , 2012 , Pages 843-855 ; 07488017 (ISSN) Davoodi, M ; Niaki, S. T. A ; Sharif University of Technology
    2012
    Abstract
    In this paper, maximum likelihood step-change point estimators of the location parameter, the out-of-control sample and the out-of-control stage are developed for auto-correlated multistage processes. To do this, the multistage process and the concept of change detection are first discussed. Then, a time-series model of the process is presented. Assuming step changes in the location parameter of the process, next, the likelihood functions of different samples before and after receiving out-of-control signal from an X-bar control chart were derived under different conditions. The maximum likelihood estimators were then obtained by maximizing the likelihood functions. Finally, the accuracy and... 

    Inference of gene regulatory networks by extended Kalman filtering using gene expression time seriesdata

    , Article BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms ; 2012 , Pages 150-155 ; 9789898425904 (ISBN) Fouladi, R ; Fatemizadeh, E ; Arab, S. S ; Sharif University of Technology
    2012
    Abstract
    In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model's parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions' true values. Through the extended Kalman... 

    Levels of complexity in turbulent time series for weakly and high Reynolds number

    , Article Physica A: Statistical Mechanics and its Applications ; Volume 391, Issue 11 , 2012 , Pages 3151-3158 ; 03784371 (ISSN) Shayeganfar, F ; Sharif University of Technology
    2012
    Abstract
    We use the detrended fluctuation analysis (DFA), the detrended cross correlation analysis (DCCA) and the magnitude and sign decomposition analysis to study the fluctuations in the turbulent time series and to probe long-term nonlinear levels of complexity in weakly and high turbulent flow. The DFA analysis indicate that there is a time scaling region in the fluctuation function, segregating regimes with different scaling exponents. We discuss that this time scaling region is related to inertial range in turbulent flows. The DCCA exponent implies the presence of power-law cross correlations. In addition, we conclude its multifractality for high Reynold's number in inertial range. Further, we... 

    The level crossing and inverse statistic analysis of German stock market index (DAX) and daily oil price time series

    , Article Physica A: Statistical Mechanics and its Applications ; Volume 391, Issue 1-2 , 2012 , Pages 209-216 ; 03784371 (ISSN) Shayeganfar, F ; Hölling, M ; Peinke, J ; Rahimi Tabar, M. Reza ; Sharif University of Technology
    2012
    Abstract
    The level crossing and inverse statistics analysis of DAX and oil price time series are given. We determine the average frequency of positive-slope crossings, να+, where Tα=1να+ is the average waiting time for observing the level α again. We estimate the probability P(K,α), which provides us the probability of observing K times of the level α with positive slope, in time scale Tα. For analyzed time series, we found that maximum K is about ≈6. We show that by using the level crossing analysis one can estimate how the DAX and oil time series will develop. We carry out the same analysis for the increments of DAX and oil price log-returns (which is known as inverse statistics), and provide the... 

    Application of ultrasonic wave technology as an asphaltene flocculation inhibition method

    , Article Saint Petersburg 2012 - Geosciences: Making the Most of the Earth's Resources ; 2012 Najafi, I ; Amani, M ; Mousavi, M. R ; Ghazanfari, M. H ; Sharif University of Technology
    Abstract
    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... 

    EEG-based functional networks in schizophrenia

    , Article Computers in Biology and Medicine ; Volume 41, Issue 12 , 2011 , Pages 1178-1186 ; 00104825 (ISSN) Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    2011
    Abstract
    Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the... 

    DNE: A method for extracting cascaded diffusion networks from social networks

    , Article Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011, 9 October 2011 through 11 October 2011 ; October , 2011 , Pages 41-48 ; 9780769545783 (ISBN) Eslami, M ; Rabiee, H. R ; Salehi, M ; Sharif University of Technology
    2011
    Abstract
    The spread of information cascades over social networks forms the diffusion networks. The latent structure of diffusion networks makes the problem of extracting diffusion links difficult. As observing the sources of information is not usually possible, the only available prior knowledge is the infection times of individuals. We confront these challenges by proposing a new method called DNE to extract the diffusion networks by using the time-series data. We model the diffusion process on information networks as a Markov random walk process and develop an algorithm to discover the most probable diffusion links. We validate our model on both synthetic and real data and show the low dependency...