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.

Need and Application of Sales Forecasting

• Human Resources

• Research & Development

• Marketing

• Finance

• Production

• Purchasing

Objective and Application

Turning This To This

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

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.

Neural Networks Models

• Feed Forward

• Recurrent

• Back-propagation

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.

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 network’s 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.

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.

Back-propagation

• Back-propagation training algorithm

• Back-propagation adjusts the weights of the NN in order to

minimize the network total mean squared error.

Base Research Paper

• 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.

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.

Thank You !

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