# Forecasting of Sales using Neural network techniques

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<ul><li> 1. Forecasting of Sales using Neural Network Techniques Data Analysis Mining Forecasting ImplementBy: Mentor: Hitesh Dua Juhi Singh Vipul Bhargava Kritika Saxena Geetu Gambhir </li></ul>
<p> 2. What is Sales Forecasting ? Forecasting is the art of estimating future demand by anticipating what buyers are likely to do under a given set of future conditions. A sales forecast is a projection into the future of expected demand given a stated set of environmental and time conditions. 3. Need and Application of Sales Forecasting Human Resources Research & Development Marketing Finance Production Purchasing 4. Objective and Application Turning This To This 5. The Process.. Setting Goals for forecasting Gathering Data Analysis Of Data Mining of Data Applying Various Neural Network Models on the data Comparing and Analysing various Neural Network Techniques Evaluation of Various Forecasting Outcomes 6. Neural Networks Artificial neural network (ANN) is a machine learning approach that models human brain and consists of a number of artificial neurons. Each neuron in ANN receives a number of inputs. An activation function is applied to these inputs which results in activation level of neuron (output value of the neuron). Knowledge about the learning task is given in the form of examples called training examples. 7. Neural Networks Models Feed Forward Recurrent Back-propagation 8. Feed Forward Model The classical learning algorithm of FFNN is based on the gradient descent method. For this reason the activation function used in FFNN are continuous functions of the weights, differentiable everywhere. The activation function for node i may be defined as a simple form of the sigmoid function in the following manner: where A > 0, Vi = Wij * Yj , such that Wij is a weight of the link from node i to node j and Yj is the output of node j. 9. Recurrent A recurrent neural network (RNN) is a class of neural network where connections between units form a directed cycle. Recurrent network can have connections that go backward from output to input nodes and models dynamic systems. In this way, a recurrent networks internal state can be altered as sets of input data are presented. It can be said to have memory. It is useful in solving problems where the solution depends not just on the current inputs but on all previous inputs. 10. Training Algorithm: Back-propagation It searches for weight values that minimize the total error of the network over the set of training examples (training set). Back-propagation consists of the repeated application of the following two passes: Forward pass: In this step, the network is activated on one example and the error of (each neuron of) the output layer is computed. Backward pass: in this step the network error is used for updating the weights. The error is propagated backwards from the output layer through the network layer by layer. This is done by recursively computing the local gradient of each neuron. 11. Back-propagation Back-propagation training algorithm Back-propagation adjusts the weights of the NN in order to minimize the network total mean squared error. 12. Base Reference Zheng Li, Renwang Li, Zhaohui Shang Haiyan Wang, Xiulan Chen, Canlin Mo, Application of BP Neural Network to Sale Forecasting for H Company, Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design pp:304-307,2012. 13. References 1. Zheng Li, Renwang Li, Zhaohui Shang Haiyan Wang, Xiulan Chen, Canlin Mo, Application of BP Neural Network to Sale Forecasting for H Company, pp:304- 307,2012. 2. Li Xia, "Sales forecasting study based on neural network , CHINAHIGH-TECH TNTERPRISES. No. 34, pp:55-56, 2010. 3. Frank M. Thiesing and Oliver Vornberger, Sales Forecasting Using Neural Networks pp:2125-2128,1997. 4. Wu Zheng-jia, Wang Wen, and Zhou Jin, "Application of BP neural network to sales forecasting for make-to-stock enterprises." Ind. Eng. J., vol. 3, pp: 105-107, Feburary 2010. 5. CHEN Yong, "Implement of sales forecasting system based on BP neural network," Comput. Inf., vo1.25, pp: 208-210, 2009. 6. Ma Rui, "Artificial neural network" , Mech. Ind. Press, 2010. 7. Zhou Zhihua, Cao Cungen, "Neural Network and Its Application," Tsinghua University Press, Beijing, 2004. 14. Thank You ! </p>

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