The Research Of Sales Prediction System Rutuja Thorat

The Research Of Sales Prediction System Rutuja Thorat ,MCA-II, VIIT, Baramati. Email [email protected] . B. Monali Kadam ,MCA-II, VIIT, Baramati. Email [email protected] Abstract sales forecast enable companies to make informed business decision and predict short-term and long-term performance. Sales forecasting system model based on Data mining, Grey-Markov prediction model, The Lead driven model, The opportunity driven model, The opportunity stages driven model. The time-series of sales are modelled multilayer perception network by using the back-propagation algorithm. For enhancement back-propagation algorithm has been two different manners such as serialized and parallelized. In this proposed model researcher define the forecasting goal, loading data, cleaning data, analysis of data, select the best model of forecast, predicting the business problem statement related to forecast and predict the forecasting value. Keywordssales forecast, time series, ANN, Data mining, back-propagation. INTRODUCTION A sales forecast is an estimation of sales volume that a company can expect to attain within the plan period. A good forecasting provides can help you develop and improve your strategic plans by increasing your knowledge of the marketplace. Sales forecasting uses a model of time series forecasting to forecast future events based on known past events to predict data points before they are measured. Time series modeling technique is used to model a series of sales data in which seasonality causes distinct spike peaks. Neural Networks can be used easier for the prediction of chaotic and noisy time series than statistical methods because they are able to learn the system dependencies on their own 1. Predictive analysis can be applied to any type of unknown whether it be in the past, present or future. Predictive analysis technology that learn from experience to predict sales of product time and get decisions. Time series models estimate difference equations containing stochastic components. Two commonly used forms of these models are autoregressive models (AR) and moving average (MA) models. Literature Survey/Previous Work In this paper, researcher presented the time needed back-propagation algorithms for batch learning implementation and calculation of the matrix products (Neurons) in two different variants serialized and parallelized. Frank M.Thiesing, Ulrich Middelberg, Oliver Vornberger, 1, The time consuming back-propagation learning has been serialized and parallelized, another approach is release the relevant time series taken into consideration. Hyung-il Ahn, W.Scott Spangler, IBM Researcher-Almaden San Jose, USA2, The trend component of the keyword frequency time series and fluctuation component of the frequency time series were significant correlation of the monthly auto sales and forecast the sales. TSAN-MING CHOI, CHI-LEUNG HUI, YONG YU3,In this paper sales data forecasting for the time series the methods of Artificial Intelligent (AI) in which a research agenda for studies around intelligent forecasting for the prediction of sales. Yu-Shui Geng, Xin-wu Du4,In order to verify the correctness of the prediction system model, Eclipe is used as the integrated development environment, Based on the available data mining tools and prediction algorithms, this article presents a sales forecast system model and Grey-markov prediction model is taken as an example to verify its feasibility theoretically. Robert Fildes, Stuart Bretschneider, Fred collopy, micheal Lawrence, Doug mark a.moon5,The forecasting of sales it looks at business forecasting from a macro perspective by suggesting a way to audit all forecasting activities within an organization. The majority of work in forecasting, it does not focus on a particular forecasting issue, but looks at business forecasting in a more holistic way. Proposed Work In our experiment sales forecasting of a data to the existing solutions increased for forecasting performance. Identify the limitations of share market data to predict place order, stock exchange, brokers, investors and traders, to forecast their data using Artificial Neural Network (ANN). Sales prediction Process Fig. Proposed sales forecast Prediction Model. Conclusion In this paper we have reviewed the intelligent fast forecasting and sales time series, the artificial neural networks are applied to a short term forecasting problems of product sales based on social media analysis and time series analysis. This article presents a sales forecast system model and Grey- Markov prediction model, ETL (EXTRACT-TRANSFORM-LOAD) tool are used in the integrated development environment. The use feed forward multilayer perceptron networks with one hidden layer and back-propagation training method. All parallelized and serialized algorithm are implement. In addition the approach in order to reduce training time is to minimize the number of input neurons. References Frank m.Thiesing, Ulrich middelberg, Oliver Varnberger, Department of Mathematics/Computer science, University Of Osnabruck, D-49069. Osnabruck, Germany. HYPERLINK [email protected] [email protected] Hyung-il Ahn and W.Scott spangler, IBM Research-Almaden, san jose, USA. Email HYPERLINK [email protected] [email protected] Email HYPERLINK [email protected] [email protected] TSAN-MING CHOI, CHI-LEUNG HUI, YONG YU Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Email HYPERLINK [email protected] [email protected], Phone-852-27666450 Yu-shui Geng, Modern Education Technology Center, Shandong Institute of Light Industry, Jinan, China, HYPERLINK [email protected] [email protected] Xin-wu Du, School of Information Science and Technology, Shandong Institute of Light Industry, Jinan, China, HYPERLINK [email protected] [email protected] Robert Fildes, Stuart Brestschneider, Fred collopy, Michael Lawrence, Doug Stewart, Heidi Winklhofer, John T.Mentzer, Mark A.Moon. School of information System, Technology nd management, University of New South Wales, Sydeny, 2052 N.S.W., Australia Cleaning Data Loading Data Define Forecasting Goals Selecting the best model for forecast Predicting business problems related to forecasting Analysis of Data Predicting value Y, 4IsNXp
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