1. Forecasting of Sales using
Neural Network Techniques
Data
Analysis
Mining
Forecasting
ImplementBy: Mentor:
Hitesh Dua Juhi Singh
Vipul Bhargava
Kritika Saxena
Geetu Gambhir

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 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.

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 !