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    Reconstruction procedure for writing down the langevin and jump-diffusion dynamics from empirical uni- and bivariate time series

    , Article Understanding Complex Systems ; 2019 , Pages 215-226 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we present the steps of reconstruction procedure for writing down the Langevin and jump diffusion stochastic dynamical equations for uni- and bivariate time series, sampled with time intervals τ. © 2019, Springer Nature Switzerland AG  

    Reconstruction Procedure for Writing Down the Langevin and Jump-Diffusion Dynamics from Empirical Uni- and Bivariate Time Series

    , Article Understanding Complex Systems ; 2019 , Pages 215-226 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we present the steps of reconstruction procedure for writing down the Langevin and jump diffusion stochastic dynamical equations for uni- and bivariate time series, sampled with time intervals τ. © 2019, Springer Nature Switzerland AG  

    Forecasting smoothed non-stationary time series using genetic algorithms

    , Article International Journal of Modern Physics C ; Volume 18, Issue 6 , 2007 , Pages 1071-1086 ; 01291831 (ISSN) Norouzzadeh, P ; Rahmani, B ; Norouzzadeh, M. S ; Sharif University of Technology
    2007
    Abstract
    We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time... 

    Approaching complexity by stochastic methods: From biological systems to turbulence

    , Article Physics Reports ; Volume 506, Issue 5 , 2011 , Pages 87-162 ; 03701573 (ISSN) Friedrich, R ; Peinke, J ; Sahimi, M ; Reza Rahimi Tabar, M ; Sharif University of Technology
    2011
    Abstract
    This review addresses a central question in the field of complex systems: given a fluctuating (in time or space), sequentially measured set of experimental data, how should one analyze the data, assess their underlying trends, and discover the characteristics of the fluctuations that generate the experimental traces? In recent years, significant progress has been made in addressing this question for a class of stochastic processes that can be modeled by Langevin equations, including additive as well as multiplicative fluctuations or noise. Important results have emerged from the analysis of temporal data for such diverse fields as neuroscience, cardiology, finance, economy, surface science,... 

    An access and inference control model for time series databases

    , Article Future Generation Computer Systems ; Volume 92 , 2019 , Pages 93-108 ; 0167739X (ISSN) Noury, A ; Amini, M ; Sharif University of Technology
    Elsevier B.V  2019
    Abstract
    Today, many applications produce and use time series data. The data of this type may contain sensitive information. So they should be protected against unauthorized accesses. In this paper, security issues of time series data are identified and an access and inference control model for satisfying the identified security requirements is proposed. Using this model, administrators can define authorization rules based on various time-based granularities (e.g. day or month) and apply value-based constraints over the accessed times series data. Furthermore, they can define policy rules over the composition of multiple time-series other than the base time-series data. Detecting and resolving... 

    Model-free chaos control in a chaotic henon-like system using takens embedding theory

    , Article 5th International Conference on Control, Instrumentation, and Automation, ICCIA 2017, 21 November 2017 through 23 November 2017 ; Volume 2018-January , 2018 , Pages 80-85 ; 9781538621349 (ISBN) Hajiloo, R ; Salarieh, H ; Alasty, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    In this paper, the problem of chaos control in a chaotic Henon-like system without using the governing equations of the system is investigated. It is also assumed that the system has only one measurable state. The time-series of the measurable state is used to stabilize chaos by a three-step method. First, using Takens embedding theory, a delayed phase space is reconstructed preserving the topological characteristics of the system. Then, an appropriate dynamic model is identified to estimate the time-series data in the reconstructed phase space. Finally, the unstable fixed point of the system is stabilized using an appropriate linear delayed feedback controller with controller gains... 

    Short-term Load Forecasting

    , M.Sc. Thesis Sharif University of Technology Shokuhian, Hamideh (Author) ; Fatemi Ardestani, Farshad (Supervisor) ; Barakchian, Mahdi (Supervisor)
    Abstract
    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 Access Control System for Time Series Data in NoSQL Databases

    , M.Sc. Thesis Sharif University of Technology Noury, Amir (Author) ; Amini, Morteza (Supervisor)
    Abstract
    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... 

    Proposing a Method for Forecasting Interrupted Time Series based on Fuzzy Logic: a System Dynamics Approach

    , M.Sc. Thesis Sharif University of Technology Modarres Vahid, Melika (Author) ; Khedmati, Majid (Supervisor)
    Abstract
    Performing analysis and forecasting is crucial. Better forecasting will lead to better decisions. One method for predicting the future is time series analysis. In reality, it is common for an intervention to occur and alter the characteristics of a time series. In recent years, interrupted time series analysis has been receiving a lot of attention. A new forecasting method for interrupted time series has been developed in this study. This is a system dynamics-based approach. At every stage of the approach, system thinking is incorporated. In order to model the effects of a given intervention, common modes of behavior in dynamic systems are used. Furthermore, control theory has been used to... 

    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  

    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... 

    Anomalous fluctuations of vertical velocity of Earth and their possible implications for earthquakes

    , Article Physical Review E - Statistical, Nonlinear, and Soft Matter Physics ; Volume 82, Issue 3 , September , 2010 ; 15393755 (ISSN) Manshour, P ; Ghasemi, F ; Matsumoto, T ; Gómez, J ; Sahimi, M ; Peinke, J ; Pacheco, A. F ; Rahimi Tabar, M. R ; Sharif University of Technology
    2010
    Abstract
    High-quality measurements of seismic activities around the world provide a wealth of data and information that are relevant to understanding of when earthquakes may occur. If viewed as complex stochastic time series, such data may be analyzed by methods that provide deeper insights into their nature, hence leading to better understanding of the data and their possible implications for earthquakes. In this paper, we provide further evidence for our recent proposal for the existence of a transition in the shape of the probability density function (PDF) of the successive detrended increments of the stochastic fluctuations of Earth's vertical velocity Vz, collected by broadband stations before... 

    Predicting the competitive position of extended gates: The case of inland customs zones

    , Article European Journal of Transport and Infrastructure Research ; Volume 18, Issue 4 , 2018 , Pages 433-456 ; 15677141 (ISSN) Sherafatipour, S ; Saffarzadeh, M ; Tavasszy, L ; Fatemi Ardestani, S. F ; Sharif University of Technology
    Editorial Board EJTIR  2018
    Abstract
    The extended gate concept aims to reduce the pressure on international ports by postponing administrative processes from these border gates to inland terminals. At present, this approach is used mainly in the container transport industry in European and Asian ports. In this paper we study an extended gate concept, where inland customs services are made available from all entry points of a country. Our aim is to predict the portion of the current flow through border gates that is diverted to these inland customs zones. We propose a time-series gravity models to predict these changes and estimate the parameters of this model using publicly available data for different cargo groups. The focus... 

    Turbulencelike behavior of seismic time series

    , Article Physical Review Letters ; Volume 102, Issue 1 , 2009 ; 00319007 (ISSN) Manshour, P ; Saberi, S ; Sahimi, M ; Peinke, J ; Pacheco, A. F ; Rahimi Tabar, M. R ; Sharif University of Technology
    2009
    Abstract
    We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes. © 2009 The American Physical Society  

    Scaling behavior in measured keystroke time series from patients with Parkinson’s disease

    , Article European Physical Journal B ; Volume 93, Issue 7 , July , 2020 Madanchi, A ; Taghavi Shahri, F ; Taghavi Shahri, S. M ; Rahimi Tabar, M. R ; Sharif University of Technology
    Springer  2020
    Abstract
    Abstract: Parkinson has remained as one of the most difficult diseases to diagnose, as there are no biomarkers to be measured, and this requires one patient to do neurological and physical examinations. As Parkinson is a progressive disease, accurate detection of its symptoms is a crucial factor for therapeutic reasons. In this study, we perform Multifractal Detrended Fluctuation Analysis (MFDFA) on measured keystroke time series for three different categories of subjects: healthy, early-PD, and De-Novo patients. We have observed different scaling behavior in terms of multifractality of the measured time series, which can be used as a practical tool for diagnosis purposes. Additionally, the... 

    Quantitative Analysis of Epileptic Seizure EEG

    , M.Sc. Thesis Sharif University of Technology Hoseini, Mahmood (Author) ; Rahimi Tabar, Mohammad Reza (Supervisor)
    Abstract
    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... 

    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... 

    Recursive spectral analysis of natural time series based on eigenvector matrix perturbation for online applications

    , Article IET Signal Processing ; Volume 5, Issue 6 , 2011 , Pages 515-526 ; 17519675 (ISSN) Mirmomeni, M ; Lucas, C ; Araabi, B. N ; Moshiri, B ; Bidar, M. R ; Sharif University of Technology
    2011
    Abstract
    Singular spectrum analysis (SSA) is a well-studied approach in signal processing. SSA has originally been designed to extract information from short noisy chaotic time series and to enhance the signal-to-noise ratio. SSA is good for offline applications; however, many applications, such as modelling, analysis, and prediction of time-varying and non-stationary time series, demand for online analysis. This study introduces a recursive algorithm called recursive SSA as a modification to regular SSA for dynamic and online applications. The proposed method is based on eigenvector matrix perturbation approach. After recursively calculating the covariance matrix of the trajectory matrix, R-SSA... 

    Fuzzy descriptor systems and spectral analysis for chaotic time series prediction

    , Article Neural Computing and Applications ; Volume 18, Issue 8 , 2009 , Pages 991-1004 ; 09410643 (ISSN) Mirmomeni, M ; Lucas, C ; Shafiee, M ; Nadjar Araabi, B ; Kamaliha, E ; Sharif University of Technology
    2009
    Abstract
    Predicting future behavior of chaotic time series and systems is a challenging area in the literature of nonlinear systems. The prediction accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. In addition, the generalization property of the proposed models trained by limited observations is of great importance. In the past two decades, singular or descriptor systems and related fuzzy descriptor models have been the subjects of interest due to their many practical applications in modeling complex phenomena. In this study fuzzy descriptor models, as a more recent neurofuzzy realization of locally linear descriptor systems, which have led to the... 

    Reconstruction of stochastic dynamical equations: exemplary diffusion, jump-diffusion processes and lévy noise-driven langevin dynamics

    , Article Understanding Complex Systems ; 2019 , Pages 227-241 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we reconstruct stochastic dynamical equations with known drift and diffusion coefficients, as well as known properties of jumps, jump amplitude and jump rate from synthetic time series, sampled with time interval τ. The examples have Langevin (white noise- and Lévy noise-driven) and jump-diffusion dynamical equations. We also study the estimation of the Kramers–Moyal coefficients for “phase” dynamics that enable us to investigate the phenomenon of synchronisation in systems with interaction. © 2019, Springer Nature Switzerland AG