Journal articles: 'Prediction of sales'

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Lyu, Xiaozhong, Cuiqing Jiang, Yong Ding, Zhao Wang, and Yao Liu. "Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions." Sustainability 11, no. 3 (February 11, 2019): 913. http://dx.doi.org/10.3390/su11030913.

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Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.

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Rezazadeh, Alireza. "A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach." Forecasting 2, no. 3 (August 6, 2020): 267–83. http://dx.doi.org/10.3390/forecast2030015.

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Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, undertaking this prediction has relied mostly on subjective human evaluations in the process of sales decision-making. In this paper, we addressed the problem of forecasting the outcome of Business to Business (B2B) sales by proposing a thorough data-driven Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to use the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.

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German, Von Kirby P., Bobby D. Gerardo, and Ruji P. Medina. "Implementing Enhanced AdaBoost Algorithm for Sales Classification and Prediction." International Journal of Trade, Economics and Finance 8, no. 6 (December 2017): 270–73. http://dx.doi.org/10.18178/ijtef.2017.8.6.577.

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Jiang, Xue Feng. "Research on the Prediction of Drug Sales Based on Levenberg-Marquardt Algorithm." Applied Mechanics and Materials 198-199 (September 2012): 1452–56. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1452.

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Prediction of drug sales trend is very important for the drug production planning and inventory. The paper studies the BP neural network and presents a kind of method based on reformative neural network to solve the issue of prediction of drug sales. Compare with traditional BP algorithm, the result reveals that this algorithm has structure rationalization and rapid constringency velocity. The experimental results demonstrate that the prediction model based on Levenberg_Marquardt algorithm is good at predicting drug sales.

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Gustriansyah, Rendra, Dana Indra Sensuse, and Arief Ramadhan. "A Sales Prediction Model Adopted the Recency-Frequency-Monetary Concept." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 3 (June 1, 2017): 711. http://dx.doi.org/10.11591/ijeecs.v6.i3.pp711-720.

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Predicting future sales is intended to control the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfillment of consumer demand can be prepared in a timely and cooperation with the supplier company can be maintained properly so that the company can avoid losing sales and customers. This study aims to propose a model to predict the sales quantity (multi-products) by adopting the Recency-Frequency-Monetary (RFM) concept and Fuzzy Analytic Hierarchy Process (FAHP) method. The measurement of sales prediction accuracy in this study using a standard measurement of Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results indicate that the average MAPE value of the model was high (3.22%), so this model can be referred to as a sales prediction model.

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GÜR ALI, ÖZDEN. "DRIVER MODERATOR METHOD FOR RETAIL SALES PREDICTION." International Journal of Information Technology & Decision Making 12, no. 06 (November 2013): 1261–86. http://dx.doi.org/10.1142/s0219622013500363.

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We introduce a new method for stock keeping unit (SKU)-store level sales prediction in the presence of promotions to support order quantity and promotion planning decisions for retail managers. The method leverages the marketing literature to generate features, and data mining techniques to train a model that provides accurate sales predictions for existing and new SKUs, as well as consistent, actionable insights into category, store and promotion dynamics. The proposed "Driver Moderator" method uses basic SKU-store information and historical sales and promotion data to generate many features. It simultaneously selects few relevant features and estimates their parameters by using an L1-norm regularized epsilon insensitive regression that is formulated to pool information across SKUs and stores. Evaluations on two grocery store databases from Turkey and the USA show that out-of-sample predictions for existing and new SKUs are as good as, or more accurate than benchmark methods. Using the method's predictions for inventory decisions doubles the inventory turn ratio versus using individual regressions by lowering lost sales and inventory levels at the same time.

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Gong, Lixiong, and Canlin Wang. "Model of Automobile Parts Sale Prediction Based on Nonlinear Periodic Gray GM(1,1) and Empirical Research." Mathematical Problems in Engineering 2019 (August 27, 2019): 1–8. http://dx.doi.org/10.1155/2019/3620120.

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The traditional predictive method cannot fully reflect the complex nonlinear characteristics and regularities of automobile and parts sales data, so the prediction precision is not high. The purpose of this paper is to propose the gray GM(1,1) nonlinear periodic predictive model by introducing the seasonal variation index to improve predictive accuracy of the single GM(1,1) model. Firstly, the paper analyzes concept of GM(1,1) and then proposes the gray GM(1,1) nonlinear periodic predictive model to forecast automobile parts sales. The model algorithm used gray theory and accumulated technology to generate new data and set up unified differential equations to find the fitting curve of automobile parts sales prediction by the seasonal variation index to remove random elements. Lastly, the gray GM(1,1) nonlinear periodic predictive model is used for empirical analysis; the result of example shows that the model proposed in the paper is feasible. The superiority of the proposed predictive model compared with the single gray GM(1,1) model is demonstrated. The reliability of this model is experienced by the accuracy test, which provides a theoretical guidance for the prediction of automobile part sales. And the average relative error is reduced by 8.52% compared with the single GM(1,1) model.

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Fadlan, Chairul, Irfan Sudahri Damanik, and Jaya Tata Hardinata. "Penerapan Metode Backpropagation Dalam Memprediksi Jumlah Penjualan Oli Shell." Prosiding Seminar Nasional Riset Information Science (SENARIS) 1 (September 30, 2019): 396. http://dx.doi.org/10.30645/senaris.v1i0.45.

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The application of a prediction is very important to do in research, so that research becomes faster and directed. Just as in predicting the number of shell oil sales, studies and the use of appropriate methods are needed to obtain optimal results. The data used in this study is sales data from PT. Mitra Petra Sejahtera Kota Medan from 2012 to 2017. The algorithm used to make this prediction is the backpropagation algorithm. This algorithm is used to predict future results based on previous data. There are 6 architectural models used in the backpropagation algorithm, among others, 4-2-1 which will later produce predictions with 83% accuracy, 4-3-1 = 78%, 4-4-1 = 83%, 4- 5-1 = 78%, 4-8-1 = 100% and 4-10-1 = 72%. The best architecture of these 6 models is 4-8-1 with an accuracy rate of 94% with a level of Error 0.001, MSE = 0.04133616. so this architectural model is good enough to be used to predict the amount of shell oil sales.

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Sianturi, Charles Jhony Mantho, Elsi Ardini, and Nita Sari Br Sembiring. "SALES FORECASTING INFORMATION SYSTEM USING THE LEAST SQUARE METHOD IN WINDI MEBEL." Jurnal Inovasi Penelitian 1, no. 2 (June 30, 2020): 75–82. http://dx.doi.org/10.47492/jip.v1i2.52.

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Windi Mebel is a business engaged in sales that sell goods and services. This home-based business was established a long time ago, but sales do not get maximum results because consumer interest has also begun to diminish due to competitors selling the same product. Therefore, with the increasingly sophisticated technology at this time it can be utilized to use a system that can forecast sales in the next few years so that the calculations generated when sales forecasting are more accurate, effective and efficient. Sales prediction system or sales forecasting can be used to estimate how much demand or demand for consumers and the market for the products produced. The more requests, the increase in sales results is also greater and as expected. To calculate the prediction of sales, a Least Square Method is applied using sales data a few years ago as a benchmark in predicting sales in the next few years. Based on these problems, the authors carry out a problem solving strategy by creating a system that uses the Least Square method to predict how much demand for furniture products the market wants in the future.

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Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 869–72. http://dx.doi.org/10.22214/ijraset.2021.38069.

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Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction

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Padilla, Washington, Jesús García, and José Molina. "Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets." Sensors 19, no. 2 (January 12, 2019): 286. http://dx.doi.org/10.3390/s19020286.

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In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered.

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Yang, Xiao. "Prediction of Cigarette Sales Amount and Average Price Based on Lunar Calendar." Advanced Materials Research 989-994 (July 2014): 5182–85. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5182.

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Prediction of cigarette sales amount and average price is important for anomaly detection of orders, customers and cigarette brands. Besides this, prediction can be support of storing and production arrangement. By analyzing large amount historical sales records, a cigarette sales amount and average price foresting model is built to predict the weekly sales based on ARIMA model which using lunar time as the calendar. Result on the five years sales data from a Chinese city indicate that sales prediction based on ARIMA using lunar time can be an effective way to improve forecasting accuracy.

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Wang, Po-Hsun, Gu-Hong Lin, and Yu-Cheng Wang. "Application of Neural Networks to Explore Manufacturing Sales Prediction." Applied Sciences 9, no. 23 (November 26, 2019): 5107. http://dx.doi.org/10.3390/app9235107.

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Manufacturing sales prediction is an important measure of national economic development trends. The plastic injection molding machine industry has its own independent R and D energy and mass production technology, with all products sold globally through international brands. However, most previous injection molding machine studies have focused on R and D, production processes, and maintenance, with little consideration of sales activity. With the development and transformation of Industry 4.0 and the impact of the global economy, Taiwan’s injection molding machine industry growth rate has gradually flattened or even declined, with company sales and profits falling below expectations. Therefore, this study collected key indicators for Taiwan’s export economy from 2008 to 2017 to help understand the impact of economic indicators on injection molding sales. We collected 35 indicators, including net entry rate of employees into manufacturing industries, trend indices, manufacturing industry sales volume indices, and customs export values. We used correlation analysis to select variables affecting plastic injection machine sales and artificial neural networks (ANN) were applied to predict injection molding machine sales at each level. Prediction results were verified against the correlation indicators, and seven key external economic factors were identified to predict accurate changes in company annual sales prediction, which will be helpful for effective resource and risk management.

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Shakti, Sana Prasanth, Mohan Kamal Hassan, Yang Zhenning, Ronnie D. Caytiles, and Iyengar N.Ch.S.N. "Annual Automobile Sales Prediction Using ARIMA Model." International Journal of Hybrid Information Technology 10, no. 6 (June 30, 2017): 13–22. http://dx.doi.org/10.14257/ijhit.2017.10.6.02.

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., Bustani, Sunu Pradana, Mulyanto ., and Nurjanana . "Prediction of electricity sales using neural based inverse distance weighting method." International Journal of Engineering & Technology 7, no. 2.2 (March 5, 2018): 65. http://dx.doi.org/10.14419/ijet.v7i2.2.12735.

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Prediction of electricity sales becomes important for State Electricity Company of Indonesia (PLN) in order to estimate the Statement of Profit and Loss in next year. To obtain good predictive results may require many variables and data availability. There are many available methods that do not require so many variables to get predicted results with a good approximation. The aim of this study was to predict electricity sales by using an interpolation method called IDW (Inverse Distance Weighting). Several data samples are mapped into Cartesian coordinates. The data samples used are power connected to the household (X-axis), to industry (Y-axis), and electricity sales (Z value). Firstly, the sampled data clustered by using SOM algorithm. The Z value in each cluster is predicted by using the IDW method. The prediction results of IDW method are then optimized using ANN-BP (Artificial Neural Network Back Propagation). The trained net structure is then used to predict the electricity sale in next year.

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Zhang, Wu Ke. "Predicting Sales with Social Media Data." Advanced Materials Research 926-930 (May 2014): 3870–73. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3870.

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With the development of social media in all over the world, some researchers have tried to predict indexes of economy and society with the social media data. And this method has been proved to be effective in the field of presidential election, stock market and macro economy. But there are no researches about the sales prediction with the data from social media. This paper selects variables from Sina MicroBlog (the biggest social media in China) and builds a predicting model with the sales data of Sony Camera. The model is tested to be significant and effective with an error rate about 13%. These results indicate that the social media data is valuable in sales forecasting.

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KLEINKNECHT, ALFRED, and GERBEN VAN DER PANNE. "PREDICTING NEW PRODUCT SALES: THE POST-LAUNCH PERFORMANCE OF 215 INNOVATORS." International Journal of Innovation Management 16, no. 02 (April 2012): 1250011. http://dx.doi.org/10.1142/s1363919611003544.

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New product sales are hard to predict. Our analysis of sales performance two years after market launch reveals that three groups of factors do not increase the accuracy of predicting new product sales: (1) A firm's general experience and experience with innovation; (2) High technological competences and strong knowledge networks; (3) Customer involvement in new product development. R&D managers should realise that experience with innovation as well as high technological competences, while possibly helpful during the development stage, do not necessarily enhance an accurate prediction of new product sales. Moreover, other than intuitively expected, networking can be ambiguous: It reduces uncertainty about future sales performance by providing information; but it may also enhance knowledge leaking to competitors, thus increasing probabilities of unexpected failure.

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Yunus, Willy, Ririn Ikana Desanti, and Wella Wella. "Data Visualization And Sales Prediction of PD. Asia Agung (Ajinomoto) Pontianak in 2019." IJNMT (International Journal of New Media Technology) 7, no. 2 (December 28, 2020): 51–57. http://dx.doi.org/10.31937/ijnmt.v7i2.1697.

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PD. Asia Agung Pontianak is the only official distributor of Ajinomoto in the West Kalimantan region. Every year this company needs to find out the amount of turnover that will be obtained in the coming year. Unfortunately, the company only makes predictions using the average income from each year which is very less accurate. This research is conduct to create visualizations and predictions using multiple linear regression methods to predict the turnover obtained in the coming year. Multiple linear regression is a regression analysis method that can use more than 2 variables in the prediction process which is divided into 2 parts, namely the dependent variable and the independent variable. The results obtained in this research are prediction results in 2019 using data from 2010 to 2018 as a basis. Prediction results show that the longer the data used the smaller the error rate obtained. The original data from the company is visualized using a dashboard on tableau software so that the data could be easier to analyze by the company.

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Subha, B. "Social Media Advertisement and its Effect in Sales Prediction - An Analysis." Shanlax International Journal of Management 8, no. 2 (October 1, 2020): 40–44. http://dx.doi.org/10.34293/management.v8i2.3263.

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Social Media Advertising plays a vital role in today’s business. It helps marketers to build relationships with their customers and increase sales. Marketers are using social media to advertise their products and generate sales. Social networking sites such as YouTube, Facebook, and Instagram are essential in today’s competitive business for boosting the sales of the firm. This study aims to predict the impact of social media advertising on sales of a company. The purpose of this paper is to build a linear regression model that predicts sales based on money spent on YouTube advertisements. The data is analyzed using the R open-source software program for statistical analysis. The R is a powerful programming tool that can represent the dataset graphically concerning different parameters, and it also uses different packages available. The result of this research shows that YouTube advertising is a better predictor of company sales.

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Muqodas, Avicienna Ulhaq, and Gede Putra Kusuma. "Promotion Scenario Based Sales Prediction on E-Retail Groceries Using Data Mining." International Journal of Emerging Technology and Advanced Engineering 11, no. 6 (June 6, 2021): 9–18. http://dx.doi.org/10.46338/ijetae0621_02.

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Mostly in many business cases, sales prediction plays an important role. Production planning is a good example. One aspect which affecting sales forecasting is promotion schedule. Since using promotion is commonly done nowadays, especially in internet business, it is hardly seen a day without promotion in Indonesian e-commerce. Thus, this study discusses about forecasting future sales based on promotion scenario data with main objective is to discover the best machine learning algorithm and model to forecast future sales. Promotion mechanism which employed in this study are price cut, buy 1-get 1, and product bundling. We use 577 data from January 2018 to July 2019 as dataset. We compare kNN, GLM, and SVR as the model predictor to forecast number of transactions in a day. From the experiment k-NN yielded the highest performance ability with squared correlation of 0.938. the worst model predictor for this case is GLM with squared correlation of 0.507. We also determine the best parameter input for each parameter using grid optimization method. We discover 2 is the best k value of kNN and Manhattan distance is the best distance calculation for this case

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Omar, Hani, Van Hai Hoang, and Duen-Ren Liu. "A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9656453.

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Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.

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Wen, Ming, Yuan Zhong, Jing Liao, Wenying Li, Hong Zhou, and Chufan Xu. "Multidimensional forecasting of electricity sales in Hunan Province based on decomposition-integration ideas." E3S Web of Conferences 300 (2021): 01020. http://dx.doi.org/10.1051/e3sconf/202130001020.

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As the focus of power companies such as State Grid Corporation of China, electricity sales forecasting is closely related to the development of enterprises and the country. The importance of accurate electricity sales forecasting in the context of electricity reform has become more and more prominent. The article takes electricity sales in Hunan Province as the research object, and constructs a more complete monthly electricity sales forecasting system based on the decomposition-integration idea, correlating electricity sales impact factors, and combining quantitative and qualitative analyses by categories. The prediction results show that the electricity sales forecasting model proposed in this paper has a high prediction accuracy under the existing data capacity level.

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Son, Young Sook. "Prediction of Electricity Sales by Time Series Modelling." Korean Journal of Applied Statistics 27, no. 3 (June 30, 2014): 419–30. http://dx.doi.org/10.5351/kjas.2014.27.3.419.

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Simon Yange, Terungwa, Charity Ojochogwu Egbunu, Oluoha Onyekwere, and Kater Amos Foga. "Prediction of Agro Products Sales Using Regression Algorithm." American Journal of Data Mining and Knowledge Discovery 5, no. 1 (2020): 11. http://dx.doi.org/10.11648/j.ajdmkd.20200501.12.

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Xia, Zhenchang, Shan Xue, Libing Wu, Jiaxin Sun, Yanjiao Chen, and Rui Zhang. "ForeXGBoost: passenger car sales prediction based on XGBoost." Distributed and Parallel Databases 38, no. 3 (May 25, 2020): 713–38. http://dx.doi.org/10.1007/s10619-020-07294-y.

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Lee, Sangjae, and Joon Yeon Choeh. "Using the Social Influence of Electronic Word-of-Mouth for Predicting Product Sales: The Moderating Effect of Review or Reviewer Helpfulness and Product Type." Sustainability 12, no. 19 (September 25, 2020): 7952. http://dx.doi.org/10.3390/su12197952.

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The social engagement of eWOM (electronic word-of-mouth) can reduce the threat of adverse selection in e-commerce. As studies that examine the social influence of eWOM are rare, the present work suggests the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales. The volume of eWOM, which is defined as the multiplication of the average length by the number of reviews, is shown to be moderated by review and reviewer helpfulness and search goods to affect product sales. Review ratings are moderated by reviewer helpfulness, and review extremity is positively (negatively) moderated by search (experience) goods and review helpfulness to affect product sales. As previous studies of differentiated sampling strategies that consider review helpfulness for predicting product sales using eWOM are lacking, this study compares the prediction power of business intelligence methods for different subsamples of products created according to high or low review and reviewer helpfulness levels. The subsample with high review or reviewer helpfulness demonstrates greater prediction performance than the subsample with low review or reviewer helpfulness when eWOM variables are used as predictors of product sales. Hence, preliminary filtering data preprocessing should consider review or reviewer helpfulness as a crucial criterion of the data quality. This will contribute to the sampling or preprocessing strategy used to predict product sales using eWOM.

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Khalil Zadeh, Neda, Mohammad Mehdi Sepehri, and Hamid Farvaresh. "Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/420310.

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One of the problems of pharmaceutical distribution companies (PDCs) is how to control inventory levels in order to prevent costs of excessive inventory and to prevent losing customers due to drug shortage. Consequently, the purpose of this study is to propose a novel method to forecast sales of PDCs. The presented method is a combination of network analysis tools and time series forecasting methods. Due to lack of enough past sales records of each drug, an explorative network based analysis is conducted to find clique sets and group members and to use comembers’ sales data in their sales prediction. Afterwards, time series sales forecasting models were built with three different approaches including ARIMA methodology, neural network, and an advanced hybrid neural network approach. The offered hybrid method by applying each drug and its comembers past records facilitates capturing both linear and nonlinear patterns of sales accurately. The performance of the proposed method was evaluated by a real dataset provided by one of the leading PDCs in Iran. The results indicated that the proposed method is able to cope with low number of past records while it forecasts medicines sales accurately.

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Dai, Wensheng, Jui-Yu Wu, and Chi-Jie Lu. "Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/438132.

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Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

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Gustriansyah, Rendra. "Single Exponential Smoothing Method to Predict Sales Multiple Products." INSIST 3, no. 2 (October 20, 2018): 176. http://dx.doi.org/10.23960/ins.v3i2.176.

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—Activity to predict sales multiple products intended for control of the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfilment of consumer demand can be cultivated in a timely and cooperation with suppliers maintained properly so that company can avoid losing sales and customers. This study aims to predict sales multiple products (6,877 products) using Single Exponential Smoothing (SES) approach, which is expected to improve the efficiency of the inventory system. Measurement accuracy of prediction in this study using a standard measurement Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results showed that the average of percentage prediction error of products using SES is high, because MAPE value obtained is 1.056% with a smoothing parameter α = 0.9

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Saputri, Nurul Adha Oktarini, and Nurul Huda. "Implementasi Sistem Informasi Prediksi Hasil Penjualan Perangkat Komputer Menggunakan Metode Double Exponential Smoothing." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 3 (July 20, 2020): 806. http://dx.doi.org/10.30865/mib.v4i3.2253.

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Prediction is an activity to predict a situation that will occur in the future by passing tests in the past. One way to get sales information in the future is to make sales forecasting. This sales forecast uses the Double Exponential Smoothing method because this method predicts by smoothing or smoothing past data by taking an average of several years to estimate the value of the coming year and this method uses the time series method. The results of this study are the right sales prediction information system, in order to determine the existing inventory of goods in accordance with the demand (demand) so that there is no overstock or lack of inventory in the future

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Tekin, Ahmet Tezcan, and Ferhan Çebi. "Click and sales prediction for OTAs’ digital advertisements: Fuzzy clustering based approach." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6619–27. http://dx.doi.org/10.3233/jifs-189123.

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Within the most productive route, online travel agencies (OTAs) intend to use advanced digital media ads to expand their piece of the industry as a whole. The metasearch engine platforms are among the most consistently used digital media environments by OTAs. Most OTAs offer day by day deals in metasearch engine platforms that are paying per click for each hotel to get reservations. The administration of offering methodologies is critical along these lines to reduce costs and increase revenue for online travel agencies. In this study, we tried to predict both the number of impressions and the regular Click-Through-Rate (CTR) level of hotel advertising for each hotel and the daily sales amount. A significant commitment of our research is to use an extended dataset generated by integrating the most informative features implemented in various related studies as the rolling average for a different amount of day and shifted values for use in the proposed test stage for CTR, impression and sales prediction. The data is created in this study by one of Turkey’s largest OTA, and we are giving OTA’s a genuine application. The results at each prediction stage show that enriching the training data with the OTA-specific additional features, which are the most insightful and sliding window techniques, improves the prediction models ’ generalization capability, and tree-based boosting algorithms carry out the greatest results on this problem. Clustering the dataset according to its specifications also improves the results of the predictions.

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Murdick, Kent. "Applications Short-Term Sales Forecasting." Mathematics Teacher 89, no. 1 (January 1996): 48–52. http://dx.doi.org/10.5951/mt.89.1.0048.

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A medical-supply company asked for help in solving a warehouse-inventory problem. They wanted a computer program to track the inventory of more than one hundred medical items, such as cases of bandages and syringes, and to predict the sales for the next business period. Thus, when the company needed to order a particular item, the quantity could be calculated automatically by the program. Specifically, the problem concerned the short-term prediction of future sales.

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Wang, Haiping. "An Insurance Sales Prediction Model Based on Deep Learning." Revue d'Intelligence Artificielle 34, no. 3 (June 30, 2020): 315–21. http://dx.doi.org/10.18280/ria.340309.

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Sucipto, Sucipto. "Sales Transaction Result Analysis for Increase Prediction of Income." Fountain of Informatics Journal 3, no. 2 (November 10, 2018): 31. http://dx.doi.org/10.21111/fij.v3i2.2286.

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Ting, Choo-Yee, Chiung Ching Ho, Hui Jia Yee, and Wan Razali Matsah. "Geospatial Analytics in Retail Site Selection and Sales Prediction." Big Data 6, no. 1 (March 2018): 42–52. http://dx.doi.org/10.1089/big.2017.0085.

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Cantón Croda, Rosa María, Damián Emilio Gibaja Romero, and Santiago Omar Caballero Morales. "Sales Prediction through Neural Networks for a Small Dataset." International Journal of Interactive Multimedia and Artificial Intelligence 5, no. 4 (2019): 35. http://dx.doi.org/10.9781/ijimai.2018.04.003.

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Saleem, Ayesha, Somia Ali, Iraj Anjum, and Fatima Anwar. "Challenges and Strategies for Sales Prediction in Apparel Industry." International Journal of Computer Science and Engineering 6, no. 8 (August 25, 2019): 7–11. http://dx.doi.org/10.14445/23488387/ijcse-v6i8p102.

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Krasonikolakis, Ioannis, Adam Vrechopoulos, and Athanasia Pouloudi. "Store selection criteria and sales prediction in virtual worlds." Information & Management 51, no. 6 (September 2014): 641–52. http://dx.doi.org/10.1016/j.im.2014.05.017.

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Chien, Chen-Fu, and Kuo-Yi Lin. "Manufacturing intelligence for Hsinchu Science Park semiconductor sales prediction." Journal of the Chinese Institute of Industrial Engineers 29, no. 2 (March 2012): 98–110. http://dx.doi.org/10.1080/10170669.2012.660200.

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Tirta, Helmy, Novario Jaya Perdana, and Bagus Mulyawan. "Sparepart sales clusterization and prediction using automatic clustering algorithm." IOP Conference Series: Materials Science and Engineering 1007 (December 31, 2020): 012191. http://dx.doi.org/10.1088/1757-899x/1007/1/012191.

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Del Giudice, Vincenzo, Benedetto Manganelli, and Pierfrancesco De Paola. "Hedonic Analysis of Housing Sales Prices with Semiparametric Methods." International Journal of Agricultural and Environmental Information Systems 8, no. 2 (April 2017): 65–77. http://dx.doi.org/10.4018/ijaeis.2017040105.

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This study estimates a hedonic price function using a semiparametric regression based on Penalized Spline Smoothing, and compares the price prediction performance with conventional parametric models. The excellent results obtained show that the semiparametric models allow to obtain a significant improvement in the prediction of housing sales prices.

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Admirani, Ica, Rachmat Gernowo, and Suryono Suryono. "Model Heuristic Time Invariant Fuzzy Time Series dan Regresi Untuk Prediksi Laba dan Analisis Variabel yang Mempengaruhi." JURNAL SISTEM INFORMASI BISNIS 6, no. 2 (December 26, 2016): 144. http://dx.doi.org/10.21456/vol6iss2pp144-153.

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Model of prediction with fuzzy time series method has ability to capture the pattern of past data to predict the fu ture of data does not need a complicated system, making it easier to use. The research aims to built prediction system using model of heuristic time invariant fuzzy time series and multiple linear regression to predict profit and analysis of variables that affect profit. Profit forecasting aims to determine the company's prospects in the future in order to remain exist in doing its business. The variables that use in the modelling are profit as the dependent variable, and sales, cost of goods sold, general and administrative expenses, selling and marketing expenses and interest income as the indepent variables. Profit forecasting modelling begins by defining universe of discourse and interval actual data of profit, then determine fuzzy set and actual data fuzzified. Furthermore, fuzzy logical relationship and fuzzy logical relationships group to fuzzified data. The prediction process consist of two prediction phase there are training phase aimed to determine trend predictor and testing phase to determine prediction results. By using 24 profit data samples resulted prediction error by using Mean Absolute Percentage Error is 11,64% and added 13 data for testing obtained prediction error is 22,27%. In analysis of variables that affect profit is known that sales variable most effect on profit than other variables with a regression coefficient 0.976.

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Geni, Bias Yulisa, Julius Santony, and Sumijan. "Prediksi Pendapatan Terbesar pada Penjualan Produk Cat dengan Menggunakan Metode Monte Carlo." Jurnal Informatika Ekonomi Bisnis 1, no. 4 (October 11, 2019): 15–20. http://dx.doi.org/10.37034/infeb.v1i4.5.

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Completing cat products in meeting consumer demand is something that must be addressed. Sales are very important for sales. The amount of demand for goods increases, it will get a large income. The purpose of this study is to predict the sales revenue of paint products at UD. Masdi Related, makes it easy for the leadership of the company to find out the amount of money obtained quickly. This research also makes it easy for companies to take business strategies quickly and optimally. The data used in this research is the data of paint product sales for January 2016 to December 2018 which is processed using the Monte carlo method. Income prediction will be done every year. In addition to predicting revenue, the sales data is also used to predict product demand every year. To predict the sales of paint products using the Monte Carlo method. The results of this study can predict sales revenue of paint products very well. Based on the results of tests conducted on the system used to predict sales revenue of cat products with an average rating of 89%. With a fairly high degree of accuracy, the application of the Monte Carlo method can be estimated to make an estimate of the income and demand for each paint product every year. Necessary, will facilitate the leadership to choose the right business strategy to increase sales of cat product sales.

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Waitz, Martin, and Andreas Mild. "IMPROVING FORECASTING ACCURACY IN CORPORATE PREDICTION MARKETS – A CASE STUDY IN THE AUSTRIAN MOBILE COMMUNICATION INDUSTRY." Journal of Prediction Markets 3, no. 3 (December 17, 2012): 49–62. http://dx.doi.org/10.5750/jpm.v3i3.467.

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Corporate prediction markets forecast business issues like market shares, sales volumes or the success rates of new product developments. The improvement of its accuracy is a major topic in prediction market research. Mostly, such markets are using a continuous double auction market mechanism. We propose a method that aggregates the data provided by such a prediction market in a different way by only accounting for the most knowledgeable market participants. We demonstrate its predictive ability with a real world experiment.We want to thank Günter Fädler from pro:kons, an Austrian provider of prediction markets, for his support and providing us with the data sets used in this paper.

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Goel, Shakti, and Rahul Bajpai. "Impact of Uncertainty in the Input Variables and Model Parameters on Predictions of a Long Short Term Memory (LSTM) Based Sales Forecasting Model." Machine Learning and Knowledge Extraction 2, no. 3 (August 15, 2020): 256–70. http://dx.doi.org/10.3390/make2030014.

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A Long Short Term Memory (LSTM) based sales model has been developed to forecast the global sales of hotel business of Travel Boutique Online Holidays (TBO Holidays). The LSTM model is a multivariate model; input to the model includes several independent variables in addition to a dependent variable, viz., sales from the previous step. One of the input variables, “number of active bookers per day”, is estimated for the same day as sales. This need for estimation requires the development of another LSTM model to predict the number of active bookers per day. The number of active bookers is variable, so the predicted is used as an input to the sales forecasting model. The use of a predicted variable as an input variable to another model increases the chance of uncertainty entering the system. This paper discusses the quantum of variability observed in sales predictions for various uncertainties or noise due to the estimation of the number of active bookers. For the purposes of this study, different noise distributions such as normalized, uniform, and logistic distributions are used, among others. Analyses of predictions demonstrate that the addition of uncertainty to the number of active bookers via dropouts as well as to the lagged sales variables leads to model predictions that are close to the observations. The least squared error between observations and predictions is higher for uncertainties modeled using other distributions (without dropouts) with the worst predictions being for Gumbel noise distribution. Gaussian noise added directly to the weights matrix yields the best results (minimum prediction errors). One possibility of this uncertainty could be that the global minimum of the least squared objective function with respect to the model weight matrix is not reached, and therefore, model parameters are not optimal. The two LSTM models used in series are also used to study the impact of corona virus on global sales. By introducing a new variable called the corona virus impact variable, the LSTM models can predict corona-affected sales within five percent (5%) of the actuals. The research discussed in the paper finds LSTM models to be effective tools that can be used in the travel industry as they are able to successfully model the trends in sales. These tools can be reliably used to simulate various hypothetical scenarios also.

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Mydyti, Hyrmet. "Data Mining Approach Improving Decision-Making Competency along the Business Digital Transformation Journey: A Case Study – Home Appliances after Sales Service." SEEU Review 16, no. 1 (June 12, 2021): 45–65. http://dx.doi.org/10.2478/seeur-2021-0008.

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Abstract Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey.

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Liang, Di, and Ya Feng Hu. "Prediction of Electric Vehicle Sales Based on Grey Linear Regression Model." Advanced Materials Research 1006-1007 (August 2014): 477–80. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.477.

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With increasing awareness of environmental protection, electric vehicle becomes more and more important in daily life. The electric vehicle sales forecast can occupy a favorable position in the unpredictably market. The grey theory is mainly applied to single exponential growth of data sequence, as well as the linear regression model needs to collect a large quantity of data. For these problems, this paper puts forward the grey linear regression model to predict the electric vehicle sales and result is calculated by using Matlab programming. Matlab is software which can carry out numerical calculation. The feasibility of this combination model is verified through the example. It can obtain higher prediction accuracy by collecting relative little data. The combination model has a lot of advantages in the electric vehicle sales prediction.

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Dai, Feng, Ming Fu, and Fu Yu Hua. "Forecast of Regional Fly Ash Production and Sales Based on BP Neural Network." Advanced Materials Research 1044-1045 (October 2014): 1749–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1749.

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With the improvement of utilization technology of fly ash, the fly ash is gradually changing from waste to important resources. Therefore, the forecast of regional fly ash production and sales is becoming more and more important to power plant operation. This paper selects Beijing-Tianjin-Tangshan area, Zhangjiakou area, Southeastern Coastal area, Western area this four typical region of China, using the 2010-2013 quarter production and sales data of fly ash as the original data sequence in the four region to build a BP neural network model for network for 2014-2015 prediction analysis. From the prediction results we can conclude that prediction accuracy conforms to the required standard, indicating that the prediction model is valid.

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Novia, C., I. Santoso, S. Soemarno, and R. Astuti. "Classification of product life cycle cluster to improve the performance of SMEs apple chips." Food Research 4, no. 6 (July 8, 2020): 1859–66. http://dx.doi.org/10.26656/fr.2017.4(6).208.

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Improved performance of apple chip SMEs in Malang Raya is strongly influenced by groups based on the product life cycle classification. The purpose of this study was to classify apple chip SMEs based on the results of the classification at the product life cycle stage, determine the prediction of apple chip sales and improve the performance of apple chip SMEs in Malang Raya. The research location was in Malang Raya area which consists of Malang Regency, Malang City, and Batu City. Data collection was obtained from thirty-one respondents who were the owners of apple chips SMEs in Malang Raya. Data analysis for cluster classification used the product life cycle stage and performance improvement using artificial neural networks for prediction of sales production and determination of dominant variables based on Cronbach's alpha and dominant indicators based on corrected item-total correlations. The results showed that stage 1 was 2 SMEs, stage 2 was 16 SMEs, stage 3 was 11 SMEs and stage 2 was SMEs. Improving the performance of apple chip SMEs in Malang Raya through sales predictions in 2019-2023 is more focused on improving innovation through the ability to see the development of consumer tastes and follow the development of technology related to product processing and marketing.

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Kristanti, Farida Titik, Sri Rahayu, and Deannes Isynuwardhana. "Integrating Capital Structure, Financial and Non-Financial Performance: Distress Prediction of SMEs." GATR Accounting and Finance Review 4, no. 2 (July 31, 2019): 56–63. http://dx.doi.org/10.35609/afr.2019.4.2(4).

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Objective – The growth of SMEs in Indonesia is rising from year to year. As an anticipation of bankruptcy, predictions can be made in an integrated means from the perspective of capital structure, financial, and non-financial performance. Methodology/Technique – A sample of 39 companies were selected using purposive sampling during the research period of 2013-2017. The results of the statistical logistic regression show that profitability is an important factor in predicting financial distress of the SMEs in Indonesia. Findings – The operating income to total assets has a negative and significant effect on SMEs financial distress. Meanwhile, retained earnings to total assets have a positive impact. Indonesian SMEs must be efficient in their operational costs to avoid financial distress. Novelty – In addition, sales are also important. If the company's sales are high, and the operational cost efficiency is maintained, the retained earnings will increase. This means that the company will be safe and able to avoid financial distress. Type of Paper: Empirical. Keywords: Capital Structure; Financial; Distress; Non-Financial; Performance. Reference to this paper should be made as follows: Kristanti F T; Rahayu S; Isynuwardhana D; 2019. Integrating Capital Structure, Financial and Non-Financial Performance: Distress Prediction of SMEs, Acc. Fin. Review 4 (2): 56 – 62 https://doi.org/10.35609/afr.2019.4.2(4) JEL Classification: G32, G33, G34.

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Journal articles on the topic 'Prediction of sales'

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