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forecasting of innovations</title> <meta name="description" content="Search results for: sales forecasting of innovations"> <meta name="keywords" content="sales forecasting of innovations"> <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" 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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="sales forecasting of innovations"> <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> 1416</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: sales forecasting of innovations</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1416</span> Applying Arima Data Mining Techniques to ERP to Generate Sales Demand Forecasting: A Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghaleb%20Y.%20Abbasi">Ghaleb Y. Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Israa%20Abu%20Rumman"> Israa Abu Rumman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20models" title="ARIMA models">ARIMA models</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20demand%20forecasting" title=" sales demand forecasting"> sales demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20code" title=" R code"> R code</a> </p> <a href="https://publications.waset.org/abstracts/64117/applying-arima-data-mining-techniques-to-erp-to-generate-sales-demand-forecasting-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64117.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">385</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">1415</span> Forecast of the Small Wind Turbines Sales with Replacement Purchases and with or without Account of Price Changes </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Churkin">V. Churkin</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Lopatin"> M. Lopatin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of the paper is to estimate the US small wind turbines market potential and forecast the small wind turbines sales in the US. The forecasting method is based on the application of the Bass model and the generalized Bass model of innovations diffusion under replacement purchases. In the work an exponential distribution is used for modeling of replacement purchases. Only one parameter of such distribution is determined by average lifetime of small wind turbines. The identification of the model parameters is based on nonlinear regression analysis on the basis of the annual sales statistics which has been published by the American Wind Energy Association (AWEA) since 2001 up to 2012. The estimation of the US average market potential of small wind turbines (for adoption purchases) without account of price changes is 57080 (confidence interval from 49294 to 64866 at P = 0.95) under average lifetime of wind turbines 15 years, and 62402 (confidence interval from 54154 to 70648 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 90,7%, while in the second - 91,8%. The effect of the wind turbines price changes on their sales was estimated using generalized Bass model. This required a price forecast. To do this, the polynomial regression function, which is based on the Berkeley Lab statistics, was used. The estimation of the US average market potential of small wind turbines (for adoption purchases) in that case is 42542 (confidence interval from 32863 to 52221 at P = 0.95) under average lifetime of wind turbines 15 years, and 47426 (confidence interval from 36092 to 58760 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 95,3%, while in the second –95,3%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bass%20model" title="bass model">bass model</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20bass%20model" title=" generalized bass model"> generalized bass model</a>, <a href="https://publications.waset.org/abstracts/search?q=replacement%20purchases" title=" replacement purchases"> replacement purchases</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20forecasting%20of%20innovations" title=" sales forecasting of innovations"> sales forecasting of innovations</a>, <a href="https://publications.waset.org/abstracts/search?q=statistics%20of%20sales%20of%20small%20wind%20turbines%20in%20the%20United%20States" title=" statistics of sales of small wind turbines in the United States"> statistics of sales of small wind turbines in the United States</a> </p> <a href="https://publications.waset.org/abstracts/25237/forecast-of-the-small-wind-turbines-sales-with-replacement-purchases-and-with-or-without-account-of-price-changes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25237.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">348</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">1414</span> The Impact of Technology on Sales Researches and Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nady%20Farag%20Faragalla%20Hanna">Nady Farag Faragalla Hanna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the car dealership industry in Japan, the sales specialist is a key factor in the success of the company. I hypothesize that when a company understands the characteristics of sales professionals in its industry, it is easier to recruit and train salespeople effectively. Lean human resources management ensures the economic success and performance of companies, especially small and medium-sized companies.The purpose of the article is to determine the characteristics of sales specialists for small and medium-sized car dealerships using the chi-square test and the proximate variable model. Accordingly, the results show that career change experience, learning ability and product knowledge are important, while university education, career building through internal transfer, leadership experience and people development are not important for becoming a sales professional. I also show that the characteristics of sales specialists are perseverance, humility, improvisation and passion for business. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electronics%20engineering" title="electronics engineering">electronics engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=sales" title=" sales"> sales</a>, <a href="https://publications.waset.org/abstracts/search?q=E-commerce%20digitalization" title=" E-commerce digitalization"> E-commerce digitalization</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20systems" title=" interactive systems"> interactive systems</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20process%20ARIMA%20models" title=" sales process ARIMA models"> sales process ARIMA models</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20demand%20forecasting" title=" sales demand forecasting"> sales demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20codetraits%20of%20sales%20professionals" title=" R codetraits of sales professionals"> R codetraits of sales professionals</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20precision%20rough%20sets%20theory" title=" variable precision rough sets theory"> variable precision rough sets theory</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20professional" title=" sales professional"> sales professional</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20professionals" title=" sales professionals"> sales professionals</a> </p> <a href="https://publications.waset.org/abstracts/185109/the-impact-of-technology-on-sales-researches-and-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185109.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">52</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">1413</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">1412</span> Walmart Sales Forecasting using Machine Learning in Python</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niyati%20%20Sharma">Niyati Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Om%20%20Anand"> Om Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjeev%20Kumar%20Prasad"> Sanjeev Kumar Prasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20algorithm" title="random forest algorithm">random forest algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression%20algorithm" title=" linear regression algorithm"> linear regression algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=extra%20trees%20classifier" title=" extra trees classifier"> extra trees classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20error" title=" mean absolute error"> mean absolute error</a> </p> <a href="https://publications.waset.org/abstracts/138978/walmart-sales-forecasting-using-machine-learning-in-python" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138978.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">1411</span> Comparing Forecasting Performances of the Bass Diffusion Model and Time Series Methods for Sales of Electric Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andreas%20Gohs">Andreas Gohs</a>, <a href="https://publications.waset.org/abstracts/search?q=Reinhold%20Kosfeld"> Reinhold Kosfeld</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study should be of interest for practitioners who want to predict precisely the sales numbers of vehicles equipped with an innovative propulsion technology as well as for researchers interested in applied (regional) time series analysis. The study is based on the numbers of new registrations of pure electric and hybrid cars. Methods of time series analysis like ARIMA are compared with the Bass Diffusion-model concerning their forecasting performances for new registrations in Germany at the national and federal state levels. Especially it is investigated if the additional information content from regional data increases the forecasting accuracy for the national level by adding predictions for the federal states. Results of parameters of the Bass Diffusion Model estimated for Germany and its sixteen federal states are reported. While the focus of this research is on the German market, estimation results are also provided for selected European and other countries. Concerning Bass-parameters and forecasting performances, we get very different results for Germany's federal states and the member states of the European Union. This corresponds to differences across the EU-member states in the adoption process of this innovative technology. Concerning the German market, the adoption is rather proceeded in southern Germany and stays behind in Eastern Germany except for Berlin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bass%20diffusion%20model" title="bass diffusion model">bass diffusion model</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicles" title=" electric vehicles"> electric vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20performance" title=" forecasting performance"> forecasting performance</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20diffusion" title=" market diffusion"> market diffusion</a> </p> <a href="https://publications.waset.org/abstracts/118712/comparing-forecasting-performances-of-the-bass-diffusion-model-and-time-series-methods-for-sales-of-electric-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118712.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">168</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">1410</span> Characteristics of Successful Sales Interaction in B2B Sales Meetings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ari%20Alam%C3%A4ki">Ari Alamäki</a>, <a href="https://publications.waset.org/abstracts/search?q=Timo%20Kaski"> Timo Kaski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The value of co-creation has gained much attention in sales research, but less is known about how salespeople and customers interact in the authentic business to business (B2B) sales meetings. The study presented in this paper empirically contributes to existing research by presenting authentic B2B sales meetings that were video recorded and analyzed using observation and qualitative content analysis methods. This paper aims to study key elements of successful sales interactions between salespeople and customers/buyers. This study points out that salespeople are selling value rather than the products or services themselves, which are only enablers in realizing business benefits. Therefore, our findings suggest that promoting and easing open discourse is an essential part of a successful sales encounter. A better understanding of how salespeople and customers successfully interact would help salespeople to develop their interpersonal sales skills. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=personal%20selling" title="personal selling">personal selling</a>, <a href="https://publications.waset.org/abstracts/search?q=relationship" title=" relationship"> relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20management" title=" sales management"> sales management</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20co-creation" title=" value co-creation"> value co-creation</a> </p> <a href="https://publications.waset.org/abstracts/23949/characteristics-of-successful-sales-interaction-in-b2b-sales-meetings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23949.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">399</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">1409</span> Enhancing Predictive Accuracy in Pharmaceutical Sales through an Ensemble Kernel Gaussian Process Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahin%20Mirshekari">Shahin Mirshekari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Moradi"> Mohammadreza Moradi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Jafari"> Hossein Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Jafari"> Mehdi Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ensaf"> Mohammad Ensaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20kernels" title=" ensemble kernels"> ensemble kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20optimization" title=" bayesian optimization"> bayesian optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pharmaceutical%20sales%20analysis" title=" pharmaceutical sales analysis"> pharmaceutical sales analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/181581/enhancing-predictive-accuracy-in-pharmaceutical-sales-through-an-ensemble-kernel-gaussian-process-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181581.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">71</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">1408</span> Interactive Systems in B2B Marketing: Perceptions of Sales Configurator Use</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tommi%20Mahlamaki">Tommi Mahlamaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Mika%20Ojala"> Mika Ojala</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digitalization is changing our lives in many ways. As consumers, we are accustomed using different online interactive sales systems. However, while many online selling sites offer systems that have evolved from simple selling functions, the change has not been as rapid in business-to-business (B2B) markets. This is because many B2B companies rely on personal sales and personal communication. The main objective of this research is to clarify perceptions towards digital interactive sales systems and, more specifically, sales configurators. It also aims to identify trends towards the use of sales configurators. To reach these objectives, an online questionnaire was created that targets Finnish B2B distributors who are, by definition, part of B2B markets. The questionnaire was sent to 340 distributors, and it was returned by 150 respondents. The results showed that 82% of respondents had heard about sales configurators, and 64% had used them. The results also showed that 48% of respondents felt that the use of sales configurators would increase in the future, while only 2% felt they would be used less. The future use of sales configurators was not seen as affecting the level of personal sales. In light of the results, we recommend that B2B companies create marketing strategies that integrate and complement traditional sales processes with digital interactive systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digitalization" title="digitalization">digitalization</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20systems" title=" interactive systems"> interactive systems</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20process" title=" sales process"> sales process</a> </p> <a href="https://publications.waset.org/abstracts/54938/interactive-systems-in-b2b-marketing-perceptions-of-sales-configurator-use" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54938.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">247</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">1407</span> Copula Markov Switching Multifractal Models for Forecasting Value-at-Risk </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giriraj%20Achari">Giriraj Achari</a>, <a href="https://publications.waset.org/abstracts/search?q=Malay%20Bhattacharyya"> Malay Bhattacharyya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the effectiveness of Copula Markov Switching Multifractal (MSM) models at forecasting Value-at-Risk of a two-stock portfolio is studied. The innovations are allowed to be drawn from distributions that can capture skewness and leptokurtosis, which are well documented empirical characteristics observed in financial returns. The candidate distributions considered for this purpose are Johnson-SU, Pearson Type-IV and α-Stable distributions. The two univariate marginal distributions are combined using the Student-t copula. The estimation of all parameters is performed by Maximum Likelihood Estimation. Finally, the models are compared in terms of accurate Value-at-Risk (VaR) forecasts using tests of unconditional coverage and independence. It is found that Copula-MSM-models with leptokurtic innovation distributions perform slightly better than Copula-MSM model with Normal innovations. Copula-MSM models, in general, produce better VaR forecasts as compared to traditional methods like Historical Simulation method, Variance-Covariance approach and Copula-Generalized Autoregressive Conditional Heteroscedasticity (Copula-GARCH) models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Copula" title="Copula">Copula</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Switching" title=" Markov Switching"> Markov Switching</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal" title=" multifractal"> multifractal</a>, <a href="https://publications.waset.org/abstracts/search?q=value-at-risk" title=" value-at-risk"> value-at-risk</a> </p> <a href="https://publications.waset.org/abstracts/115727/copula-markov-switching-multifractal-models-for-forecasting-value-at-risk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115727.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">165</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">1406</span> A New Model for Production Forecasting in ERP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20F.%20Wong">S. F. Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20I.%20Ho"> W. I. Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Lin"> B. Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20Huang"> Q. Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ERP has been used in many enterprises for management, the accuracy of the production forecasting module is vital to the decision making of the enterprise, and the profit is affected directly. Therefore, enhancing the accuracy of the production forecasting module can also increase the efficiency and profitability. To deal with a lot of data, a suitable, reliable and accurate statistics model is necessary. LSSVM and Grey System are two main models to be studied in this paper, and a case study is used to demonstrate how the combination model is effective to the result of forecasting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ERP" title="ERP">ERP</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20system" title=" grey system"> grey system</a>, <a href="https://publications.waset.org/abstracts/search?q=LSSVM" title=" LSSVM"> LSSVM</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20forecasting" title=" production forecasting"> production forecasting</a> </p> <a href="https://publications.waset.org/abstracts/3348/a-new-model-for-production-forecasting-in-erp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3348.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">463</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">1405</span> Trait of Sales Professionals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuichi%20Morita">Yuichi Morita</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshiteru%20Nakamori"> Yoshiteru Nakamori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In car dealer business of Japan, a sale professional is a key factor of company’s success. We hypothesize that, if a corporation knows what is the sales professionals’ trait of its corporation’s business field, it will be easier for a corporation to secure and nurture sales persons effectively. The lean human resources management will ensure business success and good performance of corporations, especially small and medium ones. The goal of the paper is to determine the traits of sales professionals for small-and medium-size car dealers, using chi-square test and the variable rough set model. As a result, the results illustrate that experience of job change, learning ability and product knowledge are important, and an academic background, building a career with internal transfer, experience of the leader and self-development are not important to be a sale professional. Also, we illustrate sales professionals’ traits are persistence, humility, improvisation and passion at business. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traits%20of%20sales%20professionals" title="traits of sales professionals">traits of sales professionals</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20precision%20rough%20sets%20theory" title=" variable precision rough sets theory"> variable precision rough sets theory</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20professional" title=" sales professional"> sales professional</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20professionals" title=" sales professionals"> sales professionals</a> </p> <a href="https://publications.waset.org/abstracts/12741/trait-of-sales-professionals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12741.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">382</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">1404</span> Sales Patterns Clustering Analysis on Seasonal Product Sales Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soojin%20Kim">Soojin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiwon%20Yang"> Jiwon Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungzoon%20Cho"> Sungzoon Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution" title=" distribution"> distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20pattern" title=" sales pattern"> sales pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=seasonal%20product" title=" seasonal product"> seasonal product</a> </p> <a href="https://publications.waset.org/abstracts/22411/sales-patterns-clustering-analysis-on-seasonal-product-sales-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22411.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">595</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">1403</span> The Non-Stationary BINARMA(1,1) Process with Poisson Innovations: An Application on Accident Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Sunecher">Y. Sunecher</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Mamode%20Khan"> N. Mamode Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Jowaheer"> V. Jowaheer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title="non-stationary">non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=BINARMA%281" title=" BINARMA(1"> BINARMA(1</a>, <a href="https://publications.waset.org/abstracts/search?q=1%29%20model" title="1) model">1) model</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20innovations" title=" Poisson innovations"> Poisson innovations</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20maximum%20likelihood" title=" conditional maximum likelihood"> conditional maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=CML" title=" CML"> CML</a> </p> <a href="https://publications.waset.org/abstracts/111498/the-non-stationary-binarma11-process-with-poisson-innovations-an-application-on-accident-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111498.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">129</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">1402</span> Electricity Demand Modeling and Forecasting in Singapore</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xian%20Li">Xian Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing-Guo%20Wang"> Qing-Guo Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangshuai%20Huang"> Jiangshuai Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jidong%20Liu"> Jidong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Yu"> Ming Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Kok%20Poh"> Tan Kok Poh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20industry" title="power industry">power industry</a>, <a href="https://publications.waset.org/abstracts/search?q=electricity%20demand" title=" electricity demand"> electricity demand</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/13471/electricity-demand-modeling-and-forecasting-in-singapore" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13471.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">640</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">1401</span> Load Forecasting in Short-Term Including Meteorological Variables for Balearic Islands Paper</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carolina%20Senabre">Carolina Senabre</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Valero"> Sergio Valero</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Lopez"> Miguel Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Gabaldon"> Antonio Gabaldon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=short-term%20load%20forecasting" title="short-term load forecasting">short-term load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20demand" title=" power demand"> power demand</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=load%20forecasting" title=" load forecasting"> load forecasting</a> </p> <a href="https://publications.waset.org/abstracts/107890/load-forecasting-in-short-term-including-meteorological-variables-for-balearic-islands-paper" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107890.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">190</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">1400</span> Power of Sales and Marketing in Electronics Engineering with E-commerce: Connecting the Circuits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Awais%20Kiani">Muhammad Awais Kiani</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Kiani"> Maryam Kiani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's digital age, the field of electronics engineering is experiencing unprecedented growth and innovation. To keep pace with this rapidly evolving industry, effective sales and marketing strategies are crucial, especially when combined with the power of e-commerce. This study explores the significance of integrating sales and marketing techniques with e-commerce platforms in the context of electronics engineering. It highlights the benefits, challenges, and best practices in leveraging e-commerce for sales and marketing in this industry. By embracing e-commerce, electronics engineering companies can reach a wider customer base, enhance brand visibility, and personalize customer experiences. Furthermore, this abstract delves into the importance of utilizing digital marketing tools such as search engine optimization (SEO), social media marketing, and content creation to optimize online sales. Therefore, this research aims to provide insights and recommendations for electronics engineering professionals to effectively navigate the dynamic landscape of sales and marketing in conjunction with e-commerce. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electronics%20engineering" title="electronics engineering">electronics engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=sales" title=" sales"> sales</a>, <a href="https://publications.waset.org/abstracts/search?q=E-commerce" title=" E-commerce"> E-commerce</a> </p> <a href="https://publications.waset.org/abstracts/175612/power-of-sales-and-marketing-in-electronics-engineering-with-e-commerce-connecting-the-circuits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175612.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">75</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">1399</span> Innovations in Enterprises (with References to Micro, Small and Medium Enterprises in Visakhapatnam District, India)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Lalitha%20Rani">D. Lalitha Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Sankar%20Rao"> K. Sankar Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> MSMEs, due to their unique characteristics, are found to have inherent capabilities to undertake technological and non-technological innovations successfully across industries and nations. While there is considerable empirical evidence to throw light on SME innovation contributions in the context of developed countries, there is hardly any evidence to reveal how innovative SMEs are in rapidly industrializing economies like India. Indian MSMEs are largely incremental innovators, prompted by their customers and involved in product and/or process innovations. But majority carried out innovations with internal efforts only whereas the minority which obtained external support, had better technical strength, indulged in more frequent and both product & process innovations. Such MSMEs achieved better innovation performance as well as better economic performance. Some of them internationalized themselves in the process. However such achievements are “an oasis” in the vast Indian SME sector. How to promote (i) innovations, (ii) quality of innovations and (iii) patenting culture among the SMEs is a challenge for Indian Policy Makers. However this paper examines what are the innovation practices which are being carried out in this sector and identified the barriers for innovations in this sector and concludes with proposing some policy recommendations for promoting innovations in MSME sector in India. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MSMEs" title="MSMEs">MSMEs</a>, <a href="https://publications.waset.org/abstracts/search?q=incremental%20innovators" title=" incremental innovators"> incremental innovators</a>, <a href="https://publications.waset.org/abstracts/search?q=policies" title=" policies"> policies</a>, <a href="https://publications.waset.org/abstracts/search?q=non-technological%20innovations" title=" non-technological innovations"> non-technological innovations</a> </p> <a href="https://publications.waset.org/abstracts/27607/innovations-in-enterprises-with-references-to-micro-small-and-medium-enterprises-in-visakhapatnam-district-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27607.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">479</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">1398</span> Fuzzy Time Series Forecasting Based on Fuzzy Logical Relationships, PSO Technique, and Automatic Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20M.%20Kamrul%20Islam">A. K. M. Kamrul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Bouchachia"> Abdelhamid Bouchachia</a>, <a href="https://publications.waset.org/abstracts/search?q=Suang%20Cang"> Suang Cang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongnian%20Yu"> Hongnian Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20time%20series%20%28fts%29" title="fuzzy time series (fts)">fuzzy time series (fts)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithm" title=" clustering algorithm"> clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20forecasting%20model" title=" hybrid forecasting model"> hybrid forecasting model</a> </p> <a href="https://publications.waset.org/abstracts/51515/fuzzy-time-series-forecasting-based-on-fuzzy-logical-relationships-pso-technique-and-automatic-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51515.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">250</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">1397</span> Material Saving Strategies, Technologies and Effects on Return on Sales</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jasna%20Prester">Jasna Prester</a>, <a href="https://publications.waset.org/abstracts/search?q=Najla%20Podrug"> Najla Podrug</a>, <a href="https://publications.waset.org/abstracts/search?q=Davor%20Filipovi%C4%87"> Davor Filipović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Manufacturing companies invest a significant amount of sales into material resources for production. In our sample, 58% of sales is used for manufacturing inputs, while only 24% of sales is used for salaries. This means that if a company is looking to reduce costs, the greater potential is in reduction of material costs than downsizing. This research shows that manufacturing companies in Croatia did realize material savings in last three years. It is also shown by which technologies they achieved materials cost savings. Through literature research, we found research gap as to which technologies reduce material consumption. As methodology of research four regression analyses are used to prove our findings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Croatia" title="Croatia">Croatia</a>, <a href="https://publications.waset.org/abstracts/search?q=materials%20savings%20strategies" title=" materials savings strategies"> materials savings strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=technologies" title=" technologies"> technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=return%20on%20sales" title=" return on sales"> return on sales</a> </p> <a href="https://publications.waset.org/abstracts/39863/material-saving-strategies-technologies-and-effects-on-return-on-sales" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39863.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">300</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">1396</span> Collaborative Planning and Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neha%20Asthana">Neha Asthana</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishal%20Krishna%20Prasad"> Vishal Krishna Prasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collaborative planning and forecasting are the innovative and systematic approaches towards productive integration and assimilation of data synergized into information. The changing and variable market dynamics have persuaded global business chains to incorporate collaborative planning and forecasting as an imperative tool. Thus, it is essential for the supply chains to constantly improvise, update its nature, and mould as per changing global environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20transfer" title="information transfer">information transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a> </p> <a href="https://publications.waset.org/abstracts/7060/collaborative-planning-and-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7060.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">435</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">1395</span> Sales-Based Dynamic Investment and Leverage Decisions: A Longitudinal Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rihab%20Belguith">Rihab Belguith</a>, <a href="https://publications.waset.org/abstracts/search?q=Fathi%20Abid"> Fathi Abid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper develops a system-based approach to investigate the dynamic adjustment of debt structure and investment policies of the Dow-Jones index. This approach enables the assessment of relations among sales, debt, and investment opportunities by considering the simultaneous effect of the market environmental change and future growth opportunities. We integrate the firm-specific sales variance to capture the industries' conditions in the model. Empirical results were obtained through a panel data set of firms with different sectors. The analysis support that environmental change does not affect equally the different industry since operating leverage differs among industries and so the sensitivity to sales variance. Including adjusted-specific variance, we find that there is no monotonic relation between leverage, sales, and investment. The firm may choose a low debt level in response to high sales variance but high leverage to attenuate the negative relation between sales variance and the current level of investment. We further find that while the overall effect of debt maturity on leverage is unaffected by the level of growth opportunities, the shorter the maturity of debt is, the smaller the direct effect of sales variance on investment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20panel" title="dynamic panel">dynamic panel</a>, <a href="https://publications.waset.org/abstracts/search?q=investment" title=" investment"> investment</a>, <a href="https://publications.waset.org/abstracts/search?q=leverage%20decision" title=" leverage decision"> leverage decision</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20uncertainty" title=" sales uncertainty"> sales uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/140094/sales-based-dynamic-investment-and-leverage-decisions-a-longitudinal-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140094.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">243</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">1394</span> Lee-Carter Mortality Forecasting Method with Dynamic Normal Inverse Gaussian Mortality Index </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Funda%20Kul">Funda Kul</a>, <a href="https://publications.waset.org/abstracts/search?q=%C4%B0smail%20G%C3%BCr"> İsmail Gür</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pension scheme providers have to price mortality risk by accurate mortality forecasting method. There are many mortality-forecasting methods constructed and used in literature. The Lee-Carter model is the first model to consider stochastic improvement trends in life expectancy. It is still precisely used. Mortality forecasting is done by mortality index in the Lee-Carter model. It is assumed that mortality index fits ARIMA time series model. In this paper, we propose and use dynamic normal inverse gaussian distribution to modeling mortality indes in the Lee-Carter model. Using population mortality data for Italy, France, and Turkey, the model is forecasting capability is investigated, and a comparative analysis with other models is ensured by some well-known benchmarking criterions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mortality" title="mortality">mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=lee-carter%20model" title=" lee-carter model"> lee-carter model</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20inverse%20gaussian%20distribution" title=" normal inverse gaussian distribution"> normal inverse gaussian distribution</a> </p> <a href="https://publications.waset.org/abstracts/39750/lee-carter-mortality-forecasting-method-with-dynamic-normal-inverse-gaussian-mortality-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39750.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">360</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">1393</span> Joint Optimal Pricing and Lot-Sizing Decisions for an Advance Sales System under Stochastic Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Ghoreishi">Maryam Ghoreishi</a>, <a href="https://publications.waset.org/abstracts/search?q=Christian%20Larsen"> Christian Larsen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate the effect of stochastic inputs on problem of joint optimal pricing and lot-sizing decisions where the inventory cycle is divided into advance and spot sales periods. During the advance sales period, customer can make reservations while customer with reservations can cancel their order. However, during the spot sales period customers receive the order as soon as the order is placed, but they cannot make any reservation or cancellation during that period. We assume that the inter arrival times during the advance sales and spot sales period are exponentially distributed where the arrival rate is decreasing function of price. Moreover, we assume that the number of cancelled reservations is binomially distributed. In addition, we assume that deterioration process follows an exponential distribution. We investigate two cases. First, we consider two-state case where we find the optimal price during the spot sales period and the optimal price during the advance sales period. Next, we develop a generalized case where we extend two-state case also to allow dynamic prices during the spot sales period. We apply the Markov decision theory in order to find the optimal solutions. In addition, for the generalized case, we apply the policy iteration algorithm in order to find the optimal prices, the optimal lot-size and maximum advance sales amount. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inventory%20control" title="inventory control">inventory control</a>, <a href="https://publications.waset.org/abstracts/search?q=pricing" title=" pricing"> pricing</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20decision%20theory" title=" Markov decision theory"> Markov decision theory</a>, <a href="https://publications.waset.org/abstracts/search?q=advance%20sales%20system" title=" advance sales system"> advance sales system</a> </p> <a href="https://publications.waset.org/abstracts/83282/joint-optimal-pricing-and-lot-sizing-decisions-for-an-advance-sales-system-under-stochastic-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83282.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">324</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">1392</span> Forecasting Amman Stock Market Data Using a Hybrid Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Awajan">Ahmad Awajan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadam%20Al%20Wadi"> Sadam Al Wadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Holt-Winter%20method" title="Holt-Winter method">Holt-Winter method</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</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/122857/forecasting-amman-stock-market-data-using-a-hybrid-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122857.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">129</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">1391</span> Application of Artificial Intelligence in Market and Sales Network Management: Opportunities, Benefits, and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Mahdi%20Namdari">Mohamad Mahdi Namdari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's rapidly changing and evolving business competition, companies and organizations require advanced and efficient tools to manage their markets and sales networks. Big data analysis, quick response in competitive markets, process and operations optimization, and forecasting customer behavior are among the concerns of executive managers. Artificial intelligence, as one of the emerging technologies, has provided extensive capabilities in this regard. The use of artificial intelligence in market and sales network management can lead to improved efficiency, increased decision-making accuracy, and enhanced customer satisfaction. Specifically, AI algorithms can analyze vast amounts of data, identify complex patterns, and offer strategic suggestions to improve sales performance. However, many companies are still distant from effectively leveraging this technology, and those that do face challenges in fully exploiting AI's potential in market and sales network management. It appears that the general public's and even the managerial and academic communities' lack of knowledge of this technology has caused the managerial structure to lag behind the progress and development of artificial intelligence. Additionally, high costs, fear of change and employee resistance, lack of quality data production processes, the need for updating structures and processes, implementation issues, the need for specialized skills and technical equipment, and ethical and privacy concerns are among the factors preventing widespread use of this technology in organizations. Clarifying and explaining this technology, especially to the academic, managerial, and elite communities, can pave the way for a transformative beginning. The aim of this research is to elucidate the capacities of artificial intelligence in market and sales network management, identify its opportunities and benefits, and examine the existing challenges and obstacles. This research aims to leverage AI capabilities to provide a framework for enhancing market and sales network performance for managers. The results of this research can help managers and decision-makers adopt more effective strategies for business growth and development by better understanding the capabilities and limitations of artificial intelligence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20management" title=" market management"> market management</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20network" title=" sales network"> sales network</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analysis" title=" big data analysis"> big data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-making" title=" decision-making"> decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20marketing" title=" digital marketing"> digital marketing</a> </p> <a href="https://publications.waset.org/abstracts/187963/application-of-artificial-intelligence-in-market-and-sales-network-management-opportunities-benefits-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187963.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">42</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">1390</span> Comparison of Applicability of Time Series Forecasting Models VAR, ARCH and ARMA in Management Science: Study Based on Empirical Analysis of Time Series Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Tariq">Muhammad Tariq</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammad%20Tahir"> Hammad Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawwad%20Mahmood%20Butt"> Fawwad Mahmood Butt </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20regression" title=" auto regression"> auto regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH" title=" ARCH"> ARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=ARMA" title=" ARMA"> ARMA</a> </p> <a href="https://publications.waset.org/abstracts/45124/comparison-of-applicability-of-time-series-forecasting-models-var-arch-and-arma-in-management-science-study-based-on-empirical-analysis-of-time-series-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45124.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">348</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">1389</span> Powering Connections: Synergizing Sales and Marketing for Electronics Engineering with Web Development.</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Awais%20Kiani">Muhammad Awais Kiani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Basit%20Kiani"> Abdul Basit Kiani</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Kiani"> Maryam Kiani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synergizing Sales and Marketing for Electronics Engineering with Web Development, explores the dynamic relationship between sales, marketing, and web development within the electronics engineering industry. This study is important for the power of digital platforms to connect with customers. Which increases brand visibility and drives sales. It highlights the need for collaboration between sales and marketing teams, as well as the integration of web development strategies to create seamless user experiences and effective lead generation. Furthermore, It also emphasizes the role of data analytics and customer insights in optimizing sales and marketing efforts in the ever-evolving landscape of electronics engineering. Sales and marketing play a crucial role in driving business growth, and in today's digital landscape, web development has become an integral part of these strategies. Web development enables businesses to create visually appealing and user-friendly websites that effectively showcase their products or services. It allows for the integration of e-commerce functionalities, enabling seamless online transactions. Furthermore, web development helps businesses optimize their online presence through search engine optimization (SEO) techniques, social media integration, and content management systems. This abstract highlights the symbiotic relationship between sales marketing in the electronics industry and web development, emphasizing the importance of a strong online presence in achieving business success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electronics%20industry" title="electronics industry">electronics industry</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20development" title=" web development"> web development</a>, <a href="https://publications.waset.org/abstracts/search?q=sales" title=" sales"> sales</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a> </p> <a href="https://publications.waset.org/abstracts/175613/powering-connections-synergizing-sales-and-marketing-for-electronics-engineering-with-web-development" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175613.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">1388</span> pscmsForecasting: A Python Web Service for Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Andrianakis">Ioannis Andrianakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasileios%20Gkatas"> Vasileios Gkatas</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikos%20Eleftheriadis"> Nikos Eleftheriadis</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexios%20Ellinidis"> Alexios Ellinidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ermioni%20Avramidou"> Ermioni Avramidou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> pscmsForecasting is an open-source web service that implements a variety of time series forecasting algorithms and exposes them to the user via the ubiquitous HTTP protocol. It allows developers to enhance their applications by adding time series forecasting functionalities through an intuitive and easy-to-use interface. This paper provides some background on time series forecasting and gives details about the implemented algorithms, aiming to enhance the end user’s understanding of the underlying methods before incorporating them into their applications. A detailed description of the web service’s interface and its various parameterizations is also provided. Being an open-source project, pcsmsForecasting can also be easily modified and tailored to the specific needs of each application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series" title="time series">time series</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service" title=" web service"> web service</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source" title=" open source"> open source</a> </p> <a href="https://publications.waset.org/abstracts/170621/pscmsforecasting-a-python-web-service-for-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170621.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">83</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">1387</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> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sales%20forecasting%20of%20innovations&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sales%20forecasting%20of%20innovations&page=3">3</a></li> <li class="page-item"><a class="page-link" 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