Search results for: series
862 Content Analysis and Attitude of Thai Students towards Thai Series “Hormones: Season 2”
Authors: Siriporn Meenanan
The objective of this study is to investigate the attitude of Thai students towards the Thai series "Hormones the Series Season 2". This study was conducted in the quantitative research, and the questionnaires were used to collect data from 400 people of the sample group. Descriptive statistics were used in data analysis. The findings reveal that most participants have positive comments regarding the series. They strongly agreed that the series reflects on the way of life and problems of teenagers in Thailand. Hence, the participants believe that if adults have a chance to watch the series, they will have the better understanding of the teenagers. In addition, the participants also agreed that the contents of the play are appropriate and satisfiable as the contents of “Hormones the Series Season 2” will raise awareness among the teens and use it as a guide to prevent problems that might happen during their teenage life.
Keywords: Content analysis, attitude, Thai series, Hormones the series.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 819
861 New Recursive Representations for the Favard Constants with Application to the Summation of Series
Authors: Snezhana G. Gocheva-Ilieva, Ivan H. Feschiev
Abstract:In this study integral form and new recursive formulas for Favard constants and some connected with them numeric and Fourier series are obtained. The method is based on preliminary integration of Fourier series which allows for establishing finite recursive representations for the summation. It is shown that the derived recursive representations are numerically more effective than known representations of the considered objects.
Keywords: Effective summation of series, Favard constants, finite recursive representations, Fourier seriesProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1236
860 Simulation of Series Compensated Transmission Lines Protected with Mov
Authors: Abdolamir Nekoubin
Abstract:In this paper the behavior of fixed series compensated extra high voltage transmission lines during faults is simulated. Many over-voltage protection schemes for series capacitors are limited in terms of size and performance, and are easily affected by environmental conditions. While the need for more compact and environmentally robust equipment is required. use of series capacitors for compensating part of the inductive reactance of long transmission lines increases the power transmission capacity. Emphasis is given on the impact of modern capacitor protection techniques (MOV protection). The simulation study is performed using MATLAB/SIMULINK®and results are given for a three phase and a single phase to ground fault.
Keywords: Series compensation, MOV - protected series capacitors, balanced and unbalanced faultsProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3901
859 Nonstationarity Modeling of Economic and Financial Time Series
Authors: C. Slim
Abstract:Traditional techniques for analyzing time series are based on the notion of stationarity of phenomena under study, but in reality most economic and financial series do not verify this hypothesis, which implies the implementation of specific tools for the detection of such behavior. In this paper, we study nonstationary non-seasonal time series tests in a non-exhaustive manner. We formalize the problem of nonstationary processes with numerical simulations and take stock of their statistical characteristics. The theoretical aspects of some of the most common unit root tests will be discussed. We detail the specification of the tests, showing the advantages and disadvantages of each. The empirical study focuses on the application of these tests to the exchange rate (USD/TND) and the Consumer Price Index (CPI) in Tunisia, in order to compare the Power of these tests with the characteristics of the series.
Keywords: Stationarity, unit root tests, economic time series.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 670
858 L1-Convergence of Modified Trigonometric Sums
Authors: Sandeep Kaur Chouhan, Jatinderdeep Kaur, S. S. Bhatia
Abstract:The existence of sine and cosine series as a Fourier series, their L1-convergence seems to be one of the difficult question in theory of convergence of trigonometric series in L1-metric norm. In the literature so far available, various authors have studied the L1-convergence of cosine and sine trigonometric series with special coefficients. In this paper, we present a modified cosine and sine sums and criterion for L1-convergence of these modified sums is obtained. Also, a necessary and sufficient condition for the L1-convergence of the cosine and sine series is deduced as corollaries.
Keywords: Conjugate Dirichlet kernel, Dirichlet kernel, L1-convergence, modified sums.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1094
857 Coefficients of Some Double Trigonometric Cosine and Sine Series
Authors: Jatinderdeep Kaur
Abstract:In this paper, the results of Kano from one dimensional cosine and sine series are extended to two dimensional cosine and sine series. To extend these results, some classes of coefficient sequences such as class of semi convexity and class R are extended from one dimension to two dimensions. Further, the function f(x, y) is two dimensional Fourier Cosine and Sine series or equivalently it represents an integrable function or not, has been studied. Moreover, some results are obtained which are generalization of Moricz’s results.
Keywords: Conjugate Dirichlet kernel, conjugate Fejer kernel, Fourier series, Semi-convexity.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2004
856 Investigation on Performance of Change Point Algorithm in Time Series Dynamical Regimes and Effect of Data Characteristics
Authors: Farhad Asadi, Mohammad Javad Mollakazemi
In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.
Keywords: Time series, fluctuation in statistical characteristics, optimal learning.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1656
855 2D Graphical Analysis of Wastewater Influent Capacity Time Series
Authors: Monika Chuchro, Maciej Dwornik
Abstract:The extraction of meaningful information from image could be an alternative method for time series analysis. In this paper, we propose a graphical analysis of time series grouped into table with adjusted colour scale for numerical values. The advantages of this method are also discussed. The proposed method is easy to understand and is flexible to implement the standard methods of pattern recognition and verification, especially for noisy environmental data.
Keywords: graphical analysis, time series, seasonality, noisy environmental dataProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1342
854 The Relations between the Fractal Properties of the River Networks and the River Flow Time Series
Authors: M. H. Fattahi, H. Jahangiri
Abstract:All the geophysical phenomena including river networks and flow time series are fractal events inherently and fractal patterns can be investigated through their behaviors. A non-linear system like a river basin can well be analyzed by a non-linear measure such as the fractal analysis. A bilateral study is held on the fractal properties of the river network and the river flow time series. A moving window technique is utilized to scan the fractal properties of them. Results depict both events follow the same strategy regarding to the fractal properties. Both the river network and the time series fractal dimension tend to saturate in a distinct value.
Keywords: river flow time series, fractal, river networksProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1563
853 Solution of Two-Point Nonlinear Boundary Problems Using Taylor Series Approximation and the Ying Buzu Shu Algorithm
Authors: U. C. Amadi, N. A. Udoh
One of the major challenges faced in solving initial and boundary problems is how to find approximate solutions with minimal deviation from the exact solution without so much rigor and complications. The Taylor series method provides a simple way of obtaining an infinite series which converges to the exact solution for initial value problems and this method of solution is somewhat limited for a two point boundary problem since the infinite series has to be truncated to include the boundary conditions. In this paper, the Ying Buzu Shu algorithm is used to solve a two point boundary nonlinear diffusion problem for the fourth and sixth order solution and compare their relative error and rate of convergence to the exact solution.
Keywords: Ying Buzu Shu, nonlinear boundary problem, Taylor series algorithm, infinite series.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 143
852 Effect of Implementation of Nonlinear Sequence Transformations on Power Series Expansion for a Class of Non-Linear Abel Equations
Authors: Javad Abdalkhani
Abstract:Convergence of power series solutions for a class of non-linear Abel type equations, including an equation that arises in nonlinear cooling of semi-infinite rods, is very slow inside their small radius of convergence. Beyond that the corresponding power series are wildly divergent. Implementation of nonlinear sequence transformation allow effortless evaluation of these power series on very large intervals..
Keywords: Nonlinear transformation, Abel Volterra Equations, MathematicaProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1191
851 Applying a Noise Reduction Method to Reveal Chaos in the River Flow Time Series
Authors: Mohammad H. Fattahi
Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. Noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.
Keywords: Chaotic behavior, wavelet, noise reduction, river flow.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1986
850 Differentiation of Heart Rate Time Series from Electroencephalogram and Noise
Authors: V. I. Thajudin Ahamed, P. Dhanasekaran, Paul Joseph K.
Abstract:Analysis of heart rate variability (HRV) has become a popular non-invasive tool for assessing the activities of autonomic nervous system. Most of the methods were hired from techniques used for time series analysis. Currently used methods are time domain, frequency domain, geometrical and fractal methods. A new technique, which searches for pattern repeatability in a time series, is proposed for quantifying heart rate (HR) time series. These set of indices, which are termed as pattern repeatability measure and pattern repeatability ratio are able to distinguish HR data clearly from noise and electroencephalogram (EEG). The results of analysis using these measures give an insight into the fundamental difference between the composition of HR time series with respect to EEG and noise.
Keywords: Approximate entropy, heart rate variability, noise, pattern repeatability, and sample entropy.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1615
849 Time Series Forecasting Using Independent Component Analysis
Authors: Theodor D. Popescu
Abstract:The paper presents a method for multivariate time series forecasting using Independent Component Analysis (ICA), as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then to transform back the results to the original time series space. The forecasting can be done separately and with a different method for each component, depending on its time structure. The paper gives also a review of the main algorithms for independent component analysis in the case of instantaneous mixture models, using second and high-order statistics. The method has been applied in simulation to an artificial multivariate time series with five components, generated from three sources and a mixing matrix, randomly generated.
Keywords: Independent Component Analysis, second order statistics, simulation, time series forecastingProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1654
848 Statistical Reliability Based Modeling of Series and Parallel Operating Systems using Extreme Value Theory
Authors: Mohamad Mahdavi, Mojtaba Mahdavi
Abstract:This paper tries to represent a new method for computing the reliability of a system which is arranged in series or parallel model. In this method we estimate life distribution function of whole structure using the asymptotic Extreme Value (EV) distribution of Type I, or Gumbel theory. We use EV distribution in minimal mode, for estimate the life distribution function of series structure and maximal mode for parallel system. All parameters also are estimated by Moments method. Reliability function and failure (hazard) rate and p-th percentile point of each function are determined. Other important indexes such as Mean Time to Failure (MTTF), Mean Time to repair (MTTR), for non-repairable and renewal systems in both of series and parallel structure will be computed.
Keywords: Reliability, extreme value, parallel, series, lifedistributionProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1957
847 Computational Intelligence Hybrid Learning Approach to Time Series Forecasting
Authors: Chunshien Li, Jhao-Wun Hu, Tai-Wei Chiang, Tsunghan Wu
Abstract:Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybrid learning (HL) approach for the neuro-fuzzy system, the prediction performance is excellent and the speed of learning convergence is much faster than other compared approaches. In the experiments, we use the well-known Mackey-Glass chaos time series. According to the experimental results, the prediction performance and accuracy in time series forecasting by the proposed approach is much better than other compared approaches, as shown in Table IV. Excellent prediction performance by the proposed approach has been observed.
Keywords: forecasting, hybrid learning (HL), Neuro-FuzzySystem (NFS), particle swarm optimization (PSO), recursiveleast-squares estimator (RLSE), time seriesProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1436
846 Revealing Nonlinear Couplings between Oscillators from Time Series
Authors: B.P. Bezruchko, D.A. Smirnov
Abstract:Quantitative characterization of nonlinear directional couplings between stochastic oscillators from data is considered. We suggest coupling characteristics readily interpreted from a physical viewpoint and their estimators. An expression for a statistical significance level is derived analytically that allows reliable coupling detection from a relatively short time series. Performance of the technique is demonstrated in numerical experiments.
Keywords: Nonlinear time series analysis, directional couplings, coupled oscillators.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1140
845 Fuzzy Metric Approach for Fuzzy Time Series Forecasting based on Frequency Density Based Partitioning
Authors: Tahseen Ahmed Jilani, Syed Muhammad Aqil Burney, C. Ardil
In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of enrollments at the University of Alabama. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on frequency density based partitioning of the historical enrollment data. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting enrollments than the existing methods.
Keywords: Fuzzy logical groups, fuzzified enrollments, fuzzysets, fuzzy time series.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2979
844 Forecasting Enrollment Model Based on First-Order Fuzzy Time Series
Authors: Melike Şah, Konstantin Y.Degtiarev
This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature.
Keywords: Forecasting, fuzzy time series, linguistic values, student enrollment, time-invariant model.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2120
843 Multivariate High Order Fuzzy Time Series Forecasting for Car Road Accidents
Authors: Tahseen A. Jilani, S. M. Aqil Burney, C. Ardil
Abstract:In this paper, we have presented a new multivariate fuzzy time series forecasting method. This method assumes mfactors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods of fuzzy time series forecasting. Experimentally, it is shown that our proposed method perform better than existing fuzzy time series forecasting methods. Practically, actuaries are interested in analysis of the patterns of causalities in road accidents. Thus using fuzzy time series, actuaries can define fuzzy premium and fuzzy underwriting of car insurance and life insurance for car insurance. National Institute of Statistics, Belgium provides region of risk classification for each road. Thus using this risk classification, we can predict premium rate and underwriting of insurance policy holders.
Keywords: Average forecasting error rate (AFER), Fuzziness offuzzy sets Fuzzy, If-Then rules, Multivariate fuzzy time series.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2341
842 Comparison of Artificial Neural Network Architectures in the Task of Tourism Time Series Forecast
Authors: João Paulo Teixeira, Paula Odete Fernandes
Abstract:The authors have been developing several models based on artificial neural networks, linear regression models, Box- Jenkins methodology and ARIMA models to predict the time series of tourism. The time series consist in the “Monthly Number of Guest Nights in the Hotels" of one region. Several comparisons between the different type models have been experimented as well as the features used at the entrance of the models. The Artificial Neural Network (ANN) models have always had their performance at the top of the best models. Usually the feed-forward architecture was used due to their huge application and results. In this paper the author made a comparison between different architectures of the ANNs using simply the same input. Therefore, the traditional feed-forward architecture, the cascade forwards, a recurrent Elman architecture and a radial based architecture were discussed and compared based on the task of predicting the mentioned time series.
Keywords: Artificial Neural Network Architectures, time series forecast, tourism.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1752
841 A 4-Element Corporate Series Feed Millimeter-Wave Microstrip Antenna Array for 5G Applications
Authors: G. Viswanadh Raviteja
In this paper, a microstrip antenna array is designed for 5G applications. A corporate series feed is considered to operate with a center frequency between 27 to 28 GHz to be able to cover the 5G frequency bands 24.25-27.5 GHz, 26.5-29.5 GHz and 27.5-28.35 GHz. The substrate is taken to be Rogers RT/Duroid 6002. The corporate series 5G antenna array is designed stage by stage by taking into consideration a conventional antenna designed at 28 GHz, thereby constructing the 2X1 antenna array before arriving at the final design structure of 4-element corporate series feed antenna array. The discussions concerning S11 parameter, gain and voltage standing wave ratio (VSWR) for the design structures are considered and all the important findings are tabulated. The proposed antenna array’s S11 parameter was found to be -29.00 dB at a frequency of 27.39 GHz with a good directional gain of 12.12 dB.
Keywords: Corporate series feed, millimeter wave antenna array, 5G applications, millimeter-wave (mm-wave) applicationsProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 503
840 Multi-Context Recurrent Neural Network for Time Series Applications
Authors: B. Q. Huang, Tarik Rashid, M-T. Kechadi
this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.
Keywords: Gradient descent method, recurrent neural network, learning algorithms, time series, BPProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2895
839 Implementation and Simulation of Half-Bridge Series Resonant Inverter in Zero Voltage Switching
Authors: Buket Turan Azizoğlu
Abstract:In switch mode power inverters, small sized inverters can be obtained by increasing the switching frequency. Switching frequency increment causes high driver losses. Also, high dt di and dt dv produced by the switching action creates high Electromagnetic Interference (EMI) and Radio Frequency Interference (RFI). In this paper, a series half bridge series resonant inverter circuit is simulated and evaluated practically to demonstrate the turn-on and turn-off conditions during zero or close to zero voltage switching. Also, the reverse recovery current effects of the body diode of the MOSFETs were investigated by operating above and below resonant frequency.
Keywords: Driver losses, Half Bridge series resonant inverter, Zero Voltage SwitchingProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3499
838 Influence of Technology Parameters on Properties of AA6061/SiC Composites Produced By Kobo Method
Authors: J. Wozniak, M. Kostecki, K. Broniszewski, W. Bochniak, A. Olszyna
Abstract:The influence of extrusion parameters on surface quality and properties of AA6061+x% vol. SiC (x = 0; 2,5; 5; 7,5;10) composites was discussed in this paper. The averages size of AA6061 and SiC particles were 10.6 μm and 0.42 μm, respectively. Two series of composites (I - compacts were preheated at extrusion temperature through 0.5 h and cooled by water directly after process; II - compacts were preheated through 3 hours and were not cooled) were consolidated via powder metallurgy processing and extruded by KoBo method. High values of density for both series of composites were achieved. Better surface quality was observed for II series of composites. Moreover, for these composites lower (compared to I series) but more uniform strength properties over the cross-section of the bar were noticed. Microstructure and Young-s modulus investigations were made.
Keywords: aluminum alloy, extrusion, metal matrix composites, microstructureProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1635
837 A Fuzzy Time Series Forecasting Model for Multi-Variate Forecasting Analysis with Fuzzy C-Means Clustering
Authors: Emrah Bulut, Okan Duru, Shigeru Yoshida
In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom - after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.
Keywords: C-means clustering, Fuzzy time series, Multi-variate designProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2145
836 Implementation of Neural Network Based Electricity Load Forecasting
Authors: Myint Myint Yi, Khin Sandar Linn, Marlar Kyaw
Abstract:This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. The model was evaluated with the electricity load data of Electronic Engineering Department in Mandalay Technological University in Myanmar. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in STLF.
Keywords: Neural network, Load forecast, Time series, wavelettransform.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2364
835 Detecting the Nonlinearity in Time Series from Continuous Dynamic Systems Based on Delay Vector Variance Method
Authors: Shumin Hou, Yourong Li, Sanxing Zhao
Much time series data is generally from continuous dynamic system. Firstly, this paper studies the detection of the nonlinearity of time series from continuous dynamics systems by applying the Phase-randomized surrogate algorithm. Then, the Delay Vector Variance (DVV) method is introduced into nonlinearity test. The results show that under the different sampling conditions, the opposite detection of nonlinearity is obtained via using traditional test statistics methods, which include the third-order autocovariance and the asymmetry due to time reversal. Whereas the DVV method can perform well on determining nonlinear of Lorenz signal. It indicates that the proposed method can describe the continuous dynamics signal effectively.
Keywords: Nonlinearity, Time series, continuous dynamics system, DVV methodProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1507
834 Signal Processing Approach to Study Multifractality and Singularity of Solar Wind Speed Time Series
Authors: Tushnik Sarkar, Mofazzal H. Khondekar, Subrata Banerjee
Abstract:This paper investigates the nature of the fluctuation of the daily average Solar wind speed time series collected over a period of 2492 days, from 1st January, 1997 to 28th October, 2003. The degree of self-similarity and scalability of the Solar Wind Speed signal has been explored to characterise the signal fluctuation. Multi-fractal Detrended Fluctuation Analysis (MFDFA) method has been implemented on the signal which is under investigation to perform this task. Furthermore, the singularity spectra of the signals have been also obtained to gauge the extent of the multifractality of the time series signal.
Keywords: Detrended fluctuation analysis, generalized Hurst exponent, holder exponents, multifractal exponent, multifractal spectrum, singularity spectrum, time series analysis.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1058
833 Application of Extreme Learning Machine Method for Time Series Analysis
Authors: Rampal Singh, S. Balasundaram
Abstract:In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.
Keywords: Chaotic time series, Extreme learning machine, Generalization performance.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3375