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autoregressive model</title> <meta name="description" content="Search results for: vector autoregressive model"> <meta name="keywords" content="vector autoregressive model"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="vector autoregressive model"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 17497</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: vector autoregressive model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17497</span> Diagonal Vector Autoregressive Models and Their Properties</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usoro%20Anthony%20E.">Usoro Anthony E.</a>, <a href="https://publications.waset.org/abstracts/search?q=Udoh%20Emediong"> Udoh Emediong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagonal Vector Autoregressive Models are special classes of the general vector autoregressive models identified under certain conditions, where parameters are restricted to the diagonal elements in the coefficient matrices. Variance, autocovariance, and autocorrelation properties of the upper and lower diagonal VAR models are derived. The new set of VAR models is verified with empirical data and is found to perform favourably with the general VAR models. The advantage of the diagonal models over the existing models is that the new models are parsimonious, given the reduction in the interactive coefficients of the general VAR models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VAR%20models" title="VAR models">VAR models</a>, <a href="https://publications.waset.org/abstracts/search?q=diagonal%20VAR%20models" title=" diagonal VAR models"> diagonal VAR models</a>, <a href="https://publications.waset.org/abstracts/search?q=variance" title=" variance"> variance</a>, <a href="https://publications.waset.org/abstracts/search?q=autocovariance" title=" autocovariance"> autocovariance</a>, <a href="https://publications.waset.org/abstracts/search?q=autocorrelations" title=" autocorrelations"> autocorrelations</a> </p> <a href="https://publications.waset.org/abstracts/157980/diagonal-vector-autoregressive-models-and-their-properties" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157980.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">116</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17496</span> Relationship between Food Inflation and Agriculture Lending Rate in Ghana: A Vector Autoregressive Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raymond%20K.%20Dziwornu">Raymond K. Dziwornu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lending rate of agriculture loan has persistently been high and attributed to risk in the sector. This study examined how food inflation and agriculture lending rate react to each other in Ghana using vector autoregressive approach. Quarterly data from 2006 to 2018 was obtained from the Bank of Ghana quarterly bulletin and the Ghana Statistical Service reports. The study found that a positive standard deviation shock to food inflation causes lending rate of agriculture loan to react negatively in the short run, but positively and steadily in the long run. This suggests the need to direct appropriate policy measures to reduce food inflation and consequently, the cost of credit to the agricultural sector for its growth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food%20inflation" title="food inflation">food inflation</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture" title=" agriculture"> agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=lending%20rate" title=" lending rate"> lending rate</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20autoregressive" title=" vector autoregressive"> vector autoregressive</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghana" title=" Ghana"> Ghana</a> </p> <a href="https://publications.waset.org/abstracts/115221/relationship-between-food-inflation-and-agriculture-lending-rate-in-ghana-a-vector-autoregressive-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17495</span> Identification of Classes of Bilinear Time Series Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anthony%20Usoro">Anthony Usoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20model" title="autoregressive model">autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20autoregressive%20model" title=" bilinear autoregressive model"> bilinear autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20moving%20average%20model" title=" bilinear moving average model"> bilinear moving average model</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20average%20model" title=" moving average model"> moving average model</a> </p> <a href="https://publications.waset.org/abstracts/56430/identification-of-classes-of-bilinear-time-series-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">407</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17494</span> Forecasting Stock Prices Based on the Residual Income Valuation Model: Evidence from a Time-Series Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen-Yin%20Kuo">Chen-Yin Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yung-Hsin%20Lee"> Yung-Hsin Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=residual%20income%20valuation%20model" title="residual income valuation model">residual income valuation model</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20error%20correction%20model" title=" vector error correction model"> vector error correction model</a>, <a href="https://publications.waset.org/abstracts/search?q=out%20of%20sample%20forecasting" title=" out of sample forecasting"> out of sample forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20accuracy" title=" forecasting accuracy"> forecasting accuracy</a> </p> <a href="https://publications.waset.org/abstracts/1668/forecasting-stock-prices-based-on-the-residual-income-valuation-model-evidence-from-a-time-series-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1668.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17493</span> The Sustainability of Public Debt in Taiwan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chiung-Ju%20Huang">Chiung-Ju Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines whether the Taiwan’s public debt is sustainable utilizing an unrestricted two-regime threshold autoregressive (TAR) model with an autoregressive unit root. The empirical results show that Taiwan’s public debt appears as a nonlinear series and is stationary in regime 1 but not in regime 2. This result implies that while Taiwan’s public debt was mostly sustainable over the 1996 to 2013 period examined in the study, it may no longer be sustainable in the most recent two years as the public debt ratio has increased cumulatively to 3.618%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonlinearity" title="nonlinearity">nonlinearity</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20debt" title=" public debt"> public debt</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability" title=" sustainability"> sustainability</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20autoregressive%20model" title=" threshold autoregressive model"> threshold autoregressive model</a> </p> <a href="https://publications.waset.org/abstracts/10069/the-sustainability-of-public-debt-in-taiwan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17492</span> Assessing Functional Structure in European Marine Ecosystems Using a Vector-Autoregressive Spatio-Temporal Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katyana%20A.%20Vert-Pre">Katyana A. Vert-Pre</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20T.%20Thorson"> James T. Thorson</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Trancart"> Thomas Trancart</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Feunteun"> Eric Feunteun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In marine ecosystems, spatial and temporal species structure is an important component of ecosystems’ response to anthropological and environmental factors. Although spatial distribution patterns and fish temporal series of abundance have been studied in the past, little research has been allocated to the joint dynamic spatio-temporal functional patterns in marine ecosystems and their use in multispecies management and conservation. Each species represents a function to the ecosystem, and the distribution of these species might not be random. A heterogeneous functional distribution will lead to a more resilient ecosystem to external factors. Applying a Vector-Autoregressive Spatio-Temporal (VAST) model for count data, we estimate the spatio-temporal distribution, shift in time, and abundance of 140 species of the Eastern English Chanel, Bay of Biscay and Mediterranean Sea. From the model outputs, we determined spatio-temporal clusters, calculating p-values for hierarchical clustering via multiscale bootstrap resampling. Then, we designed a functional map given the defined cluster. We found that the species distribution within the ecosystem was not random. Indeed, species evolved in space and time in clusters. Moreover, these clusters remained similar over time deriving from the fact that species of a same cluster often shifted in sync, keeping the overall structure of the ecosystem similar overtime. Knowing the co-existing species within these clusters could help with predicting data-poor species distribution and abundance. Further analysis is being performed to assess the ecological functions represented in each cluster. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cluster%20distribution%20shift" title="cluster distribution shift">cluster distribution shift</a>, <a href="https://publications.waset.org/abstracts/search?q=European%20marine%20ecosystems" title=" European marine ecosystems"> European marine ecosystems</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20distribution" title=" functional distribution"> functional distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20model" title=" spatio-temporal model"> spatio-temporal model</a> </p> <a href="https://publications.waset.org/abstracts/87029/assessing-functional-structure-in-european-marine-ecosystems-using-a-vector-autoregressive-spatio-temporal-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87029.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">193</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17491</span> The Ability of Forecasting the Term Structure of Interest Rates Based on Nelson-Siegel and Svensson Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tea%20Poklepovi%C4%87">Tea Poklepović</a>, <a href="https://publications.waset.org/abstracts/search?q=Zdravka%20Aljinovi%C4%87"> Zdravka Aljinović</a>, <a href="https://publications.waset.org/abstracts/search?q=Branka%20Marasovi%C4%87"> Branka Marasović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector auto-regressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is neural networks using Nelson-Siegel estimation of yield curves. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nelson-Siegel%20Model" title="Nelson-Siegel Model">Nelson-Siegel Model</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Svensson%20Model" title=" Svensson Model"> Svensson Model</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20autoregressive%20model" title=" vector autoregressive model"> vector autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=yield%20curve" title=" yield curve"> yield curve</a> </p> <a href="https://publications.waset.org/abstracts/2460/the-ability-of-forecasting-the-term-structure-of-interest-rates-based-on-nelson-siegel-and-svensson-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17490</span> New Estimation in Autoregressive Models with Exponential White Noise by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman%20Suparman">Suparman Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A white noise in autoregressive (AR) model is often assumed to be normally distributed. In application, the white noise usually do not follows a normal distribution. This paper aims to estimate a parameter of AR model that has a exponential white noise. A Bayesian method is adopted. A prior distribution of the parameter of AR model is selected and then this prior distribution is combined with a likelihood function of data to get a posterior distribution. Based on this posterior distribution, a Bayesian estimator for the parameter of AR model is estimated. Because the order of AR model is considered a parameter, this Bayesian estimator cannot be explicitly calculated. To resolve this problem, a method of reversible jump Markov Chain Monte Carlo (MCMC) is adopted. A result is a estimation of the parameter AR model can be simultaneously calculated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20%28AR%29%20model" title="autoregressive (AR) model">autoregressive (AR) model</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20white%20Noise" title=" exponential white Noise"> exponential white Noise</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20Markov%20Chain%20Monte%20Carlo%20%28MCMC%29" title=" reversible jump Markov Chain Monte Carlo (MCMC)"> reversible jump Markov Chain Monte Carlo (MCMC)</a> </p> <a href="https://publications.waset.org/abstracts/71720/new-estimation-in-autoregressive-models-with-exponential-white-noise-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17489</span> Measuring Financial Asset Return and Volatility Spillovers, with Application to Sovereign Bond, Equity, Foreign Exchange and Commodity Markets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Petra%20Palic">Petra Palic</a>, <a href="https://publications.waset.org/abstracts/search?q=Maruska%20Vizek"> Maruska Vizek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We provide an in-depth analysis of interdependence of asset returns and volatilities in developed and developing countries. The analysis is split into three parts. In the first part, we use multivariate GARCH model in order to provide stylized facts on cross-market volatility spillovers. In the second part, we use a generalized vector autoregressive methodology developed by Diebold and Yilmaz (2009) in order to estimate separate measures of return spillovers and volatility spillovers among sovereign bond, equity, foreign exchange and commodity markets. In particular, our analysis is focused on cross-market return, and volatility spillovers in 19 developed and developing countries. In order to estimate named spillovers, we use daily data from 2008 to 2017. In the third part of the analysis, we use a generalized vector autoregressive framework in order to estimate total and directional volatility spillovers. We use the same daily data span for one developed and one developing country in order to characterize daily volatility spillovers across stock, bond, foreign exchange and commodities markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-market%20spillovers" title="cross-market spillovers">cross-market spillovers</a>, <a href="https://publications.waset.org/abstracts/search?q=sovereign%20bond%20markets" title=" sovereign bond markets"> sovereign bond markets</a>, <a href="https://publications.waset.org/abstracts/search?q=equity%20markets" title=" equity markets"> equity markets</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk%20%28VAR%29" title=" value at risk (VAR)"> value at risk (VAR)</a> </p> <a href="https://publications.waset.org/abstracts/72158/measuring-financial-asset-return-and-volatility-spillovers-with-application-to-sovereign-bond-equity-foreign-exchange-and-commodity-markets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72158.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17488</span> Estimating Lost Digital Video Frames Using Unidirectional and Bidirectional Estimation Based on Autoregressive Time Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Navid%20Daryasafar">Navid Daryasafar</a>, <a href="https://publications.waset.org/abstracts/search?q=Nima%20Farshidfar"> Nima Farshidfar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we make attempt to hide error in video with an emphasis on the time-wise use of autoregressive (AR) models. To resolve this problem, we assume that all information in one or more video frames is lost. Then, lost frames are estimated using analogous Pixels time information in successive frames. Accordingly, after presenting autoregressive models and how they are applied to estimate lost frames, two general methods are presented for using these models. The first method which is the same standard method of autoregressive models estimates lost frame in unidirectional form. Usually, in such condition, previous frames information is used for estimating lost frame. Yet, in the second method, information from the previous and next frames is used for estimating the lost frame. As a result, this method is known as bidirectional estimation. Then, carrying out a series of tests, performance of each method is assessed in different modes. And, results are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=error%20steganography" title="error steganography">error steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=unidirectional%20estimation" title=" unidirectional estimation"> unidirectional estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=bidirectional%20estimation" title=" bidirectional estimation"> bidirectional estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=AR%20linear%20estimation" title=" AR linear estimation"> AR linear estimation</a> </p> <a href="https://publications.waset.org/abstracts/14175/estimating-lost-digital-video-frames-using-unidirectional-and-bidirectional-estimation-based-on-autoregressive-time-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14175.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">539</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17487</span> Monitoring Systemic Risk in the Hedge Fund Sector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Frank%20Hespeler">Frank Hespeler</a>, <a href="https://publications.waset.org/abstracts/search?q=Giuseppe%20Loiacono"> Giuseppe Loiacono</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose measures for systemic risk generated through intra-sectorial interdependencies in the hedge fund sector. These measures are based on variations in the average cross-effects of funds showing significant interdependency between their individual returns and the moments of the sector’s return distribution. The proposed measures display a high ability to identify periods of financial distress, are robust to modifications in the underlying econometric model and are consistent with intuitive interpretation of the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hedge%20funds" title="hedge funds">hedge funds</a>, <a href="https://publications.waset.org/abstracts/search?q=systemic%20risk" title=" systemic risk"> systemic risk</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20autoregressive%20model" title=" vector autoregressive model"> vector autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20monitoring" title=" risk monitoring"> risk monitoring</a> </p> <a href="https://publications.waset.org/abstracts/49577/monitoring-systemic-risk-in-the-hedge-fund-sector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49577.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17486</span> Life Prediction Method of Lithium-Ion Battery Based on Grey Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaogang%20Li">Xiaogang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Jieqiong%20Miao"> Jieqiong Miao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Grey%20prediction%20model" title="Grey prediction model">Grey prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=trigonometric%20functions" title=" trigonometric functions"> trigonometric functions</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square%20error" title=" root mean square error"> root mean square error</a> </p> <a href="https://publications.waset.org/abstracts/29370/life-prediction-method-of-lithium-ion-battery-based-on-grey-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">461</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17485</span> Application of Generalized Autoregressive Score Model to Stock Returns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katleho%20Daniel%20Makatjane">Katleho Daniel Makatjane</a>, <a href="https://publications.waset.org/abstracts/search?q=Diteboho%20Lawrence%20Xaba"> Diteboho Lawrence Xaba</a>, <a href="https://publications.waset.org/abstracts/search?q=Ntebogang%20Dinah%20Moroke"> Ntebogang Dinah Moroke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current study investigates the behaviour of time-varying parameters that are based on the score function of the predictive model density at time t. The mechanism to update the parameters over time is the scaled score of the likelihood function. The results revealed that there is high persistence of time-varying, as the location parameter is higher and the skewness parameter implied the departure of scale parameter from the normality with the unconditional parameter as 1.5. The results also revealed that there is a perseverance of the leptokurtic behaviour in stock returns which implies the returns are heavily tailed. Prior to model estimation, the White Neural Network test exposed that the stock price can be modelled by a GAS model. Finally, we proposed further researches specifically to model the existence of time-varying parameters with a more detailed model that encounters the heavy tail distribution of the series and computes the risk measure associated with the returns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20autoregressive%20score%20model" title="generalized autoregressive score model">generalized autoregressive score model</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20returns" title=" stock returns"> stock returns</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying" title=" time-varying"> time-varying</a> </p> <a href="https://publications.waset.org/abstracts/78817/application-of-generalized-autoregressive-score-model-to-stock-returns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78817.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">500</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17484</span> Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panudet%20Saengseedam">Panudet Saengseedam</a>, <a href="https://publications.waset.org/abstracts/search?q=Nanthachai%20Kantanantha"> Nanthachai Kantanantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20conditional%20autoregressive%20model" title=" multivariate conditional autoregressive model"> multivariate conditional autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20time%20series" title=" spatial time series"> spatial time series</a> </p> <a href="https://publications.waset.org/abstracts/11875/spatial-time-series-models-for-rice-and-cassava-yields-based-on-bayesian-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11875.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17483</span> Space Vector PWM and Model Predictive Control for Voltage Source Inverter Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a comparative assessment of Space Vector Pulse Width Modulation (SVPWM) and Model Predictive Control (MPC) for two-level three phase (2L-3P) Voltage Source Inverter (VSI). VSI with associated system is subjected to both control techniques and the results are compared. Matlab/Simulink was used to model, simulate and validate the control schemes. Findings of this study show that MPC is superior to SVPWM in terms of total harmonic distortion (THD) and implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=voltage%20source%20inverter" title="voltage source inverter">voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20vector%20pulse%20width%20modulation" title=" space vector pulse width modulation"> space vector pulse width modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=comparison" title=" comparison"> comparison</a> </p> <a href="https://publications.waset.org/abstracts/16220/space-vector-pwm-and-model-predictive-control-for-voltage-source-inverter-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16220.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">508</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17482</span> Combined Odd Pair Autoregressive Coefficients for Epileptic EEG Signals Classification by Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukari%20Nassim">Boukari Nassim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signals%20classification" title=" EEG signals classification"> EEG signals classification</a>, <a href="https://publications.waset.org/abstracts/search?q=combined%20odd%20pair%20autoregressive%20coefficients" title=" combined odd pair autoregressive coefficients"> combined odd pair autoregressive coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a> </p> <a href="https://publications.waset.org/abstracts/47454/combined-odd-pair-autoregressive-coefficients-for-epileptic-eeg-signals-classification-by-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17481</span> Vector Quantization Based on Vector Difference Scheme for Image Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Biji%20Jacob">Biji Jacob</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vector quantization algorithm which uses minimum distance calculation for codebook generation, a time consuming calculation performed on each pixel values leads to computation complexity. The codebook is updated by comparing the distance of each vector to their centroid vector and measure for their closeness. In this paper vector quantization is modified based on vector difference algorithm for image enhancement purpose. In the proposed scheme, vector differences between the vectors are considered as the new generation vectors or new codebook vectors. The codebook is updated by comparing the new generation vector with a threshold value having minimum error with the parent vector. The minimum error decides the fitness of each newly generated vector. Thus the codebook is generated in an adaptive manner and the fitness value is determined for the suppression of the degraded portion of the image and thereby leads to the enhancement of the image through the adaptive searching capability of the vector quantization through vector difference algorithm. Experimental results shows that the vector difference scheme efficiently modifies the vector quantization algorithm for enhancing the image with peak signal to noise ratio (PSNR), mean square error (MSE), Euclidean distance (E_dist) as the performance parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=codebook" title="codebook">codebook</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20difference" title=" vector difference"> vector difference</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20quantization" title=" vector quantization"> vector quantization</a> </p> <a href="https://publications.waset.org/abstracts/39597/vector-quantization-based-on-vector-difference-scheme-for-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39597.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">267</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17480</span> Imprecise Vector: The Case of Subnormality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dhruba%20Das">Dhruba Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, the author has put forward the actual mathematical explanation of subnormal imprecise vector. Every subnormal imprecise vector has to be defined with reference to a membership surface. The membership surface of normal imprecise vector has already defined based on Randomness-Impreciseness Consistency Principle. The Randomness- Impreciseness Consistency Principle leads to defining a normal law of impreciseness using two different laws of randomness. A normal imprecise vector is a special case of subnormal imprecise vector. Nothing however is available in the literature about the membership surface when a subnormal imprecise vector is defined. The author has shown here how to construct the membership surface of a subnormal imprecise vector. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=imprecise%20vector" title="imprecise vector">imprecise vector</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20surface" title=" membership surface"> membership surface</a>, <a href="https://publications.waset.org/abstracts/search?q=subnormal%20imprecise%20number" title=" subnormal imprecise number"> subnormal imprecise number</a>, <a href="https://publications.waset.org/abstracts/search?q=subnormal%20imprecise%20vector" title=" subnormal imprecise vector"> subnormal imprecise vector</a> </p> <a href="https://publications.waset.org/abstracts/44144/imprecise-vector-the-case-of-subnormality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17479</span> Composite Forecasts Accuracy for Automobile Sales in Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watchareeporn%20Chaimongkol">Watchareeporn Chaimongkol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we compare the statistical measures accuracy of composite forecasting model to estimate automobile customer demand in Thailand. A modified simple exponential smoothing and autoregressive integrate moving average (ARIMA) forecasting model is built to estimate customer demand of passenger cars, instead of using information of historical sales data. Our model takes into account special characteristic of the Thai automobile market such as sales promotion, advertising and publicity, petrol price, and interest rate for loan. We evaluate our forecasting model by comparing forecasts with actual data using six accuracy measurements, mean absolute percentage error (MAPE), geometric mean absolute error (GMAE), symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), median relative absolute error (MdRAE), and geometric mean relative absolute error (GMRAE). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite%20forecasting" title="composite forecasting">composite forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20exponential%20smoothing%20model" title=" simple exponential smoothing model"> simple exponential smoothing model</a>, <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20integrate%20moving%20average%20model%20selection" title=" autoregressive integrate moving average model selection"> autoregressive integrate moving average model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy%20measurements" title=" accuracy measurements"> accuracy measurements</a> </p> <a href="https://publications.waset.org/abstracts/6189/composite-forecasts-accuracy-for-automobile-sales-in-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">362</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17478</span> The Acquisition of Case in Biological Domain Based on Text Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shen%20Jian">Shen Jian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hu%20Jie"> Hu Jie</a>, <a href="https://publications.waset.org/abstracts/search?q=Qi%20Jin"> Qi Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Liu%20Wei%20Jie"> Liu Wei Jie</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Ji%20Yi"> Chen Ji Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Ying%20Hong"> Peng Ying Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to settle the problem of acquiring case in biological related to design problems, a biometrics instance acquisition method based on text mining is presented. Through the construction of corpus text vector space and knowledge mining, the feature selection, similarity measure and case retrieval method of text in the field of biology are studied. First, we establish a vector space model of the corpus in the biological field and complete the preprocessing steps. Then, the corpus is retrieved by using the vector space model combined with the functional keywords to obtain the biological domain examples related to the design problems. Finally, we verify the validity of this method by taking the example of text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20space%20model" title=" vector space model"> vector space model</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=biologically%20inspired%20design" title=" biologically inspired design"> biologically inspired design</a> </p> <a href="https://publications.waset.org/abstracts/88075/the-acquisition-of-case-in-biological-domain-based-on-text-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88075.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17477</span> Determination of the Axial-Vector from an Extended Linear Sigma Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Sayed%20Taha%20Ali">Tarek Sayed Taha Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dependence of the axial-vector coupling constant gA on the quark masses has been investigated in the frame work of the extended linear sigma model. The field equations have been solved in the mean-field approximation. Our study shows a better fitting to the experimental data compared with the existing models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extended%20linear%20sigma%20model" title="extended linear sigma model">extended linear sigma model</a>, <a href="https://publications.waset.org/abstracts/search?q=nucleon%20properties" title=" nucleon properties"> nucleon properties</a>, <a href="https://publications.waset.org/abstracts/search?q=axial%20coupling%20constant" title=" axial coupling constant"> axial coupling constant</a>, <a href="https://publications.waset.org/abstracts/search?q=physic" title=" physic"> physic</a> </p> <a href="https://publications.waset.org/abstracts/2310/determination-of-the-axial-vector-from-an-extended-linear-sigma-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17476</span> A Survey on Quasi-Likelihood Estimation Approaches for Longitudinal Set-ups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naushad%20Mamode%20Khan">Naushad Mamode Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Com-Poisson (CMP) model is one of the most popular discrete generalized linear models (GLMS) that handles both equi-, over- and under-dispersed data. In longitudinal context, an integer-valued autoregressive (INAR(1)) process that incorporates covariate specification has been developed to model longitudinal CMP counts. However, the joint likelihood CMP function is difficult to specify and thus restricts the likelihood based estimating methodology. The joint generalized quasilikelihood approach (GQL-I) was instead considered but is rather computationally intensive and may not even estimate the regression effects due to a complex and frequently ill conditioned covariance structure. This paper proposes a new GQL approach for estimating the regression parameters (GQLIII) that are based on a single score vector representation. The performance of GQL-III is compared with GQL-I and separate marginal GQLs (GQL-II) through some simulation experiments and is proved to yield equally efficient estimates as GQL-I and is far more computationally stable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=longitudinal" title="longitudinal">longitudinal</a>, <a href="https://publications.waset.org/abstracts/search?q=com-Poisson" title=" com-Poisson"> com-Poisson</a>, <a href="https://publications.waset.org/abstracts/search?q=ill-conditioned" title=" ill-conditioned"> ill-conditioned</a>, <a href="https://publications.waset.org/abstracts/search?q=INAR%281%29" title=" INAR(1)"> INAR(1)</a>, <a href="https://publications.waset.org/abstracts/search?q=GLMS" title=" GLMS"> GLMS</a>, <a href="https://publications.waset.org/abstracts/search?q=GQL" title=" GQL"> GQL</a> </p> <a href="https://publications.waset.org/abstracts/40051/a-survey-on-quasi-likelihood-estimation-approaches-for-longitudinal-set-ups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40051.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17475</span> The Impact of Bitcoin on Stock Market Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oliver%20Takawira">Oliver Takawira</a>, <a href="https://publications.waset.org/abstracts/search?q=Thembi%20Hope"> Thembi Hope</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study will analyse the relationship between Bitcoin price movements and the Johannesburg stock exchange (JSE). The aim is to determine whether Bitcoin price movements affect the stock market performance. As crypto currencies continue to gain prominence as a safe asset during periods of economic distress, this raises the question of whether Bitcoin’s prosperity could affect investment in the stock market. To identify the existence of a short run and long run linear relationship, the study will apply the Autoregressive Distributed Lag Model (ARDL) bounds test and a Vector Error Correction Model (VECM) after testing the data for unit roots and cointegration using the Augmented Dicker Fuller (ADF) and Phillips-Perron (PP). The Non-Linear Auto Regressive Distributed Lag (NARDL) will then be used to check if there is a non-linear relationship between bitcoin prices and stock market prices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bitcoin" title="bitcoin">bitcoin</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title=" stock market"> stock market</a>, <a href="https://publications.waset.org/abstracts/search?q=interest%20rates" title=" interest rates"> interest rates</a>, <a href="https://publications.waset.org/abstracts/search?q=ARDL" title=" ARDL"> ARDL</a> </p> <a href="https://publications.waset.org/abstracts/150006/the-impact-of-bitcoin-on-stock-market-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150006.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17474</span> Combined Effect of Heat Stimulation and Delay Addition of Superplasticizer with Slag on Fresh and Hardened Property of Mortar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antoni%20Wibowo">Antoni Wibowo</a>, <a href="https://publications.waset.org/abstracts/search?q=Harry%20Pujianto"> Harry Pujianto</a>, <a href="https://publications.waset.org/abstracts/search?q=Dewi%20Retno%20Sari%20Saputro"> Dewi Retno Sari Saputro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (<em>NARX)</em> to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NARX%20%28Nonlinear%20Autoregressive%20Exogenous%20Model%29" title="NARX (Nonlinear Autoregressive Exogenous Model)">NARX (Nonlinear Autoregressive Exogenous Model)</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title=" stock market"> stock market</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/77854/combined-effect-of-heat-stimulation-and-delay-addition-of-superplasticizer-with-slag-on-fresh-and-hardened-property-of-mortar" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17473</span> One-Class Support Vector Machine for Sentiment Analysis of Movie Review Documents </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chothmal">Chothmal</a>, <a href="https://publications.waset.org/abstracts/search?q=Basant%20Agarwal"> Basant Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection%20methods" title="feature selection methods">feature selection methods</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=NB" title=" NB"> NB</a>, <a href="https://publications.waset.org/abstracts/search?q=one-class%20SVM" title=" one-class SVM"> one-class SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/37674/one-class-support-vector-machine-for-sentiment-analysis-of-movie-review-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37674.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">517</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17472</span> Predicting Returns Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shay%20Kee%20Tan">Shay Kee Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kok%20Haur%20Ng"> Kok Haur Ng</a>, <a href="https://publications.waset.org/abstracts/search?q=Jennifer%20So-Kuen%20Chan"> Jennifer So-Kuen Chan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper extends the conditional autoregressive range (CARR) model to multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain in-sample estimates and forecasts of volatilities for these individual and pairwise sum series. Then covariances are calculated to construct the fitted variance-covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets’ returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions with the scaled realised open-to-close volatility measure as the proxy for the unobserved true volatility. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba, Engle, Kraft, and Kroner type of generalized autoregressive conditional heteroskedasticity (BEKK-GARCH) models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using Kupiec test to confirm the accuracy of the VaR forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=range-based%20volatility" title="range-based volatility">range-based volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation" title=" correlation"> correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20CARR-return%20model" title=" multivariate CARR-return model"> multivariate CARR-return model</a>, <a href="https://publications.waset.org/abstracts/search?q=value-at-risk" title=" value-at-risk"> value-at-risk</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20value-at-risk" title=" conditional value-at-risk"> conditional value-at-risk</a> </p> <a href="https://publications.waset.org/abstracts/159359/predicting-returns-volatilities-and-correlations-of-stock-indices-using-multivariate-conditional-autoregressive-range-and-return-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">99</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17471</span> Stock Market Prediction by Regression Model with Social Moods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masahiro%20Ohmura">Masahiro Ohmura</a>, <a href="https://publications.waset.org/abstracts/search?q=Koh%20Kakusho"> Koh Kakusho</a>, <a href="https://publications.waset.org/abstracts/search?q=Takeshi%20Okadome"> Takeshi Okadome</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title="stock market prediction">stock market prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20moods" title=" social moods"> social moods</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20model" title=" regression model"> regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=DJIA" title=" DJIA"> DJIA</a> </p> <a href="https://publications.waset.org/abstracts/8713/stock-market-prediction-by-regression-model-with-social-moods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8713.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">548</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17470</span> Bayesian Flexibility Modelling of the Conditional Autoregressive Prior in a Disease Mapping Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davies%20Obaromi">Davies Obaromi</a>, <a href="https://publications.waset.org/abstracts/search?q=Qin%20Yongsong"> Qin Yongsong</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Ndege"> James Ndege</a>, <a href="https://publications.waset.org/abstracts/search?q=Azeez%20Adeboye"> Azeez Adeboye</a>, <a href="https://publications.waset.org/abstracts/search?q=Akinwumi%20Odeyemi"> Akinwumi Odeyemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Besag2" title="Besag2">Besag2</a>, <a href="https://publications.waset.org/abstracts/search?q=CAR%20models" title=" CAR models"> CAR models</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20mapping" title=" disease mapping"> disease mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20models" title=" spatial models"> spatial models</a> </p> <a href="https://publications.waset.org/abstracts/77683/bayesian-flexibility-modelling-of-the-conditional-autoregressive-prior-in-a-disease-mapping-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17469</span> Linkages Between Climate Change, Agricultural Productivity, Food Security and Economic Growth</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jih%C3%A8ne%20Khalifa">Jihène Khalifa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study analyzed the relationships between Tunisia’s economic growth, food security, agricultural productivity, and climate change using the ARDL model for the period from 1990 to 2022. The ARDL model reveals a positive correlation between economic growth and lagged agricultural productivity. Additionally, the vector autoregressive (VAR) model highlights the beneficial impact of lagged agricultural productivity on economic growth and the negative effect of rainfall on economic growth. Granger causality analysis identifies unidirectional relationships from economic growth to agricultural productivity, crop production, food security, and temperature variations, as well as from temperature variations to crop production. Furthermore, a bidirectional causality is established between crop production and food security. The study underscores the impact of climate change on crop production and suggests the need for adaptive strategies to mitigate these climate effects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic%20growth" title="economic growth">economic growth</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture" title=" agriculture"> agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20security" title=" food security"> food security</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title=" climate change"> climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=ARDl" title=" ARDl"> ARDl</a>, <a href="https://publications.waset.org/abstracts/search?q=VAR" title=" VAR"> VAR</a> </p> <a href="https://publications.waset.org/abstracts/189244/linkages-between-climate-change-agricultural-productivity-food-security-and-economic-growth" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189244.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">31</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17468</span> Intracellular Strategies for Gene Delivery into Mammalian Cells Using Bacteria as a Vector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kumaran%20Narayanan">Kumaran Narayanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20N.%20Osahor"> Andrew N. Osahor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E. coli has been engineered by our group and by others as a vector to deliver DNA into cultured human and animal cells. However, so far conditions to improve gene delivery using this vector have not been investigated, resulting in a major gap in our understanding of the requirements for this vector to function optimally. Our group recently published novel data showing that simple addition of the DNA transfection reagent Lipofectamine increased the efficiency of the E. coli vector by almost 3-fold, providing the first strong evidence that further optimization of bactofection is possible. This presentation will discuss advances that demonstrate the effects of several intracellular strategies that improve the efficiency of this vector. Conditions that promote endosomal escape of internalized bacteria to evade lysosomal destruction after entry in the cell, a known obstacle limiting this vector, are elucidated. Further, treatments that increase bacterial lysis so that the vector can release its transgene into the mammalian environment for expression will be discussed. These experiments will provide valuable new insight to advance this E. coli system as an important class of vector technology for genetic correction of human disease models in cells and whole animals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA" title="DNA">DNA</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20coli" title=" E. coli"> E. coli</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression" title=" gene expression"> gene expression</a>, <a href="https://publications.waset.org/abstracts/search?q=vector" title=" vector"> vector</a> </p> <a href="https://publications.waset.org/abstracts/45408/intracellular-strategies-for-gene-delivery-into-mammalian-cells-using-bacteria-as-a-vector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light 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