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    The Kramers–Moyal coefficients of non-stationary time series and in the presence of microstructure (measurement) noise

    , Article Understanding Complex Systems ; 2019 , Pages 181-189 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Most real world time series have transient behaviours and are non-stationary. They exhibit different type of non-stationarities, such as trends, cycles, random-walking, and generally exhibit strong intermittency. Therefore local stochastic characteristics of time series, such as the drift and diffusion coefficients, as well as the jump rate and jump amplitude, will provide very important information for understanding and quantifying “real time” variability of time series. For diffusive processes the systems have a longer memory and a higher correlation time scale and, therefore, one expects the stochastic features of dynamics to change slowly. In contrast, a rapid change of dynamics with... 

    Distinguishing diffusive and jumpy behaviors in real-world time series

    , Article Understanding Complex Systems ; 2019 , Pages 207-213 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Jumps are discontinuous variations in time series and with large amplitude can be considered as an extreme event. We expect the higher the jump activity to cause higher uncertainty in the stochastic behaviour of measured time series. Therefore, building statistical evidence to detect real jump seems of primary importance. In addition jump events can participate in the observed non-Gaussian feature of the increments’ (ramp up and ramp down) statistics of many time series [1]. This is the reason that most of the jump detection techniques are based on threshold values for differential of time series. There is not, however, a robust method for detection and characterisation of such discontinuous... 

    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  

    The kramers–moyal coefficients of non-stationary time series and in the presence of microstructure (measurement) noise

    , Article Understanding Complex Systems ; 2019 , Pages 181-189 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Most real world time series have transient behaviours and are non-stationary. They exhibit different type of non-stationarities, such as trends, cycles, random-walking, and generally exhibit strong intermittency. Therefore local stochastic characteristics of time series, such as the drift and diffusion coefficients, as well as the jump rate and jump amplitude, will provide very important information for understanding and quantifying “real time” variability of time series. For diffusive processes the systems have a longer memory and a higher correlation time scale and, therefore, one expects the stochastic features of dynamics to change slowly. In contrast, a rapid change of dynamics with... 

    Distinguishing diffusive and jumpy behaviors in real-world time series

    , Article Understanding Complex Systems ; 2019 , Pages 207-213 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Jumps are discontinuous variations in time series and with large amplitude can be considered as an extreme event. We expect the higher the jump activity to cause higher uncertainty in the stochastic behaviour of measured time series. Therefore, building statistical evidence to detect real jump seems of primary importance. In addition jump events can participate in the observed non-Gaussian feature of the increments’ (ramp up and ramp down) statistics of many time series [1]. This is the reason that most of the jump detection techniques are based on threshold values for differential of time series. There is not, however, a robust method for detection and characterisation of such discontinuous... 

    Short-term prediction of air pollution using TD-CMAC neural network model

    , Article Soft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Sixth Biannual World Automation Congress, WAC 2004, Sevilla, 28 June 2004 through 1 July 2004 ; 2004 , Pages 357-362 ; 1889335231 (ISBN) Rahmani, A. M ; Teshnehlab, M ; Abbaspour, M ; Setayeshi, S ; Sharif University of Technology
    2004
    Abstract
    This paper presents a new model to short-term prediction of air pollution using a new structure is based on the intelligent neural networks. A new structure known as Time Delay Cerebellar Model Arithmetic Computer (TD-CMAC), an extension to the CMAC, it requires fewer memory sizes. The new model is demonstrated and validated with three primary air pollutants known as carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO 2). The simulation results for the half an hour ahead-prediction of the air pollutant data set show that the suggested new model is suitable for our purpose  

    Impact of mobility on COVID-19 spread – A time series analysis

    , Article Transportation Research Interdisciplinary Perspectives ; Volume 13 , 2022 ; 25901982 (ISSN) Zargari, F ; Aminpour, N ; Ahmadian, M. A ; Samimi, A ; Saidi, S ; Sharif University of Technology
    Elsevier Ltd  2022
    Abstract
    In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in... 

    Short Term Traffic State Forecasting for Travel Time Estimation

    , M.Sc. Thesis Sharif University of Technology Badrestani, Ebrahim (Author) ; Beigy, Hamid (Supervisor)
    Abstract
    Real-time travel time estimation is a major requirement in many transportation related systems. One of the main challeges is to estimate the traffic speed and then forecast it for a short time. A valuable data source for this task is instant location of moving cars that is captured using global positioning system (GPS) and sent through internet in online manner. The main problem is that the resulting traffic data is severely sparse and also contains a lot of noise. Previous researchs on this type of data are mostly based on matrix or tensor factorization. In this work it is shown that despite the large fraction of missing value it is possible to use neural network for this problem with some... 

    Evaluating the Impact of Gasoline Price Change on the Passing Car Volume in the Provinces of Iran and Tehran and the Impact of CBD Entry Policy Change on the Passing Car Volume in Tehran

    , M.Sc. Thesis Sharif University of Technology Oshanreh, Mohammad Mehdi (Author) ; Amini, Zahra (Supervisor)
    Abstract
    Nowadays, various policies are adopted by transportation managers and planners. These policies aim to improve system performance, reduce user costs, control and reduce air pollution, reduce noise pollution, and ultimately reduce congestion. A set of these policies in the form of transportation demand management is presented in the literature. A common way to find the effect of a policy on user behavior is to use questionnaires. Other causal inference models have been proposed in disciplines such as statistics, political science, marketing science, epidemiology, and psychology. The purpose of these models is to find the causal effect of an intervention (treatment) on a system. These studies... 

    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... 

    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... 

    Implementing spectral decomposition of time series data in artificial neural networks to predict air pollutant concentrations

    , Article Environmental Engineering Science ; Volume 32, Issue 5 , January , 2015 , Pages 379-388 ; 10928758 (ISSN) Kamali, N ; Zare Shahne, M ; Arhami, M ; Sharif University of Technology
    Mary Ann Liebert Inc  2015
    Abstract
    A model to predict air pollutants' concentrations was developed by implementing spectral decomposition of time series data, obtained by Kolmogorov-Zurbenko filter, in Artificial Neural Networks (ANN). This model was utilized to separate and individually predict three spectral components of air pollutants' time series of short, seasonal, and long-term. The best set of input variable was selected by evaluating the significance of different input variables while modeling different time series components. Moreover, different possible approaches for constructing such models were examined. Performance of the constructed model to predict air pollutants' level at a central location in Tehran, Iran,... 

    Developing a time series model based on particle swarm optimization for gold price forecasting

    , Article Proceedings - 3rd International Conference on Business Intelligence and Financial Engineering, BIFE 2010, 13 August 2010 through 15 August 2010, Hong Kong ; August , 2010 , Pages 337-340 ; 9780769541167 (ISBN) Hadavandi, E ; Ghanbari, A ; Abbasian Naghneh, S ; Sharif University of Technology
    2010
    Abstract
    The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

    An intelligent ACO-SA approach for short term electricity load prediction

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 18 August 2010 through 21 August 2010 ; Volume 6216 LNAI , 2010 , Pages 623-633 ; 03029743 (ISSN) ; 9783642149313 (ISBN) Ghanbari, A ; Hadavandi, E ; Abbasian Naghneh, S ; Huang D. S ; Zhang X ; Sharif University of Technology
    2010
    Abstract
    Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. On the other hand, electrical load prediction is one of the important concerns of power systems so development of intelligent prediction tools for performing accurate predictions is essential. This study presents an intelligent hybrid approach called ACO-SA by hybridization of Ant Colony Optimization (ACO) and Simulated Annealing (SA). The hybrid approach consists of two general stages. At the first stage time series inputs will be fed into ACO and it performs a global search to find a globally optimum solution.... 

    Seasonal fractal-scaling of floods in two U.S. water resources regions

    , Article Journal of Hydrology ; Volume 540 , 2016 , Pages 232-239 ; 00221694 (ISSN) Alipour, M. H ; Tayefeh Rezakhani, A ; Shamsai, A ; Sharif University of Technology
    Elsevier  2016
    Abstract
    Understanding the behavior and estimating the magnitude of floods with specific recurrence intervals are important tasks for various applications such as flood protection strategies. Fractal analysis has proven useful in characterization of flood frequency behavior. We employ a systematic fractal approach which enables dividing streamflow data into different behavior regimes and, in particular, identifying flood regimes. Since seasonality is a key factor in flood-formation scenarios, we incorporate this concept in our analysis through generating two separate streamflow data sets for summer and winter, and next performing associated fractal analysis on each. To illustrate our approach and see... 

    On the control of unknown continuous time chaotic systems by applying takens embedding theory

    , Article Chaos, Solitons and Fractals ; Volume 109 , April , 2018 , Pages 53-57 ; 09600779 (ISSN) Kaveh, H ; Salarieh, H ; Hajiloo, R ; Sharif University of Technology
    Elsevier Ltd  2018
    Abstract
    In this paper, a new approach to control continuous time chaotic systems with an unknown governing equation and limitation on the measurement of states, has been investigated. In many chaotic systems, disability to measure all of the states is a usual limitation, like in some economical, biological and many other engineering systems. Takens showed that a chaotic attractor has an astonishing feature in which it can embed to a mathematically similar attractor by using time series of one of the states. The new embedded attractor saves much information from the original attractor. This phenomenon has been deployed to present a new way to control continuous time chaotic systems, when only one of... 

    Detection of streamflow trends and variability in karun river-Iran as parts of climate change and climate variability

    , Article Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers, 17 May 2009 through 21 May 2009, Kansas City, MO ; Volume 342 , 2009 , Pages 4782-4793 ; 9780784410363 (ISBN) Farrokhi, A. R ; Abrishamchi, A ; Sharif University of Technology
    2009
    Abstract
    This paper describes the application of statistical and spectral procedures that identifies trends and periodicity in streamflow time series. The results of Mann-Kendall and seasonal Kendall tests (non-parametric tests which are known as appropriate tools in detecting linear trends of hydrological time series) shows negative trends especially during low water months (August to November). This downward trend is more significant in October. But these methods can not interpret periodic behavior. Hence spectral procedures were applied on data series to investigate periodicities in streamflow data series. Fourier and Continuous Wavelet Transform (CWT) analyses produce evidence of interannual... 

    Transportation development and globalization trends: A comparative global assessment

    , Article 1st International Symposium on Transportation and Development Innovative Best Practices 2008, TDIBP 2008, Beijing, 24 April 2008 through 26 April 2008 ; Volume 319 , 2008 , Pages X8-14 ; 9780784409619 (ISBN) Vaziri, M ; Rezaee, A ; Sharif University of Technology
    2008
    Abstract
    Globalization is shaping a new socio-economic order with profound, and still unfolding, implications for transportation development. A prerequisite for national competitiveness in the global market place is efficacious transportation when its recent technological advances have created an unprecedented rise in mobility and accessibility. Developments of efficient and effective transportation infrastructure and services are among the principal driving forces for the globalization process. Using international databanks, this paper describes an attempt to shed some light on national trends of globalization and transportation, and their relationships. Deploying a comparative macroscopic approach... 

    Dynamic time warping-based features with class-specific joint importance maps for action recognition using kinect depth sensor

    , Article IEEE Sensors Journal ; Volume 21, Issue 7 , 2021 , Pages 9300-9313 ; 1530437X (ISSN) Mohammadzade, H ; Hosseini, S ; Rezaei Dastjerdehei, M. R ; Tabejamaat, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    Abstract
    This paper proposes a novel 3D action recognition technique that uses time-series information extracted from depth image sequences for use in systems of human daily activity monitoring. To this end, each action is represented as a multi-dimensional time series, where each dimension represents the position variation of one skeleton joint over time. The time series is then mapped onto a vector space using Dynamic Time Warping (DTW) distance. Furthermore, to employ the correlation-distinctiveness relationship of the sequences in recognition, this vector space is remapped onto a discriminative space using the regularized Fisher method, where final decisions about the actions are made. Unlike... 

    Modeling the accuracy of traffic crash prediction models

    , Article IATSS Research ; Volume 46, Issue 3 , 2022 , Pages 345-352 ; 03861112 (ISSN) Rashidi, M. H ; Keshavarz, S ; Pazari, P ; Safahieh, N ; Samimi, A ; Sharif University of Technology
    Elsevier B.V  2022
    Abstract
    Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant...