Generating forecast is undoubtedly one of the most important sector in any industry. It not only helps the demand planner of the company which is focusing primarily on the future forecasting of the sales but also the inventory management and its handling cost. Down the line of the companys supply chain, a wrong forecast can impact in an alternate way of incurring heavy cost. Apart from this a correct forecasting leading to a good accuracy says how the profit and loss of the company depends upon. This heavily depends upon the predicting machine that the company is using for the forecasting. Demand planners look for a consistently accurate results over a period of time. Where the accuracy of the forecast is not only good but also the algorithm is stable in a long period of time. This we have illustrated through the analysis and the model takes care of the stability of a particular algorithm selected for a SKU. In this paper we want to highlight a new ensemble technique using the averaging method which not only gives priority to the algorithm which consistently maintains a good accuracy but also decreases the deviation from the actual sales. Based upon the history it tries to give the importance to the one which predicts better and penalizes other algorithms which deviate from the actual sales.

1. TFFN: Two Hidden Layer Feed Forward Network using the
randomness of Extreme Learning Machine
Authors:
Nimai Chand Das Adhikari
Arpana Alka
Dr. Raju K George
Indian Institute of Space Science and Technology, Trivandrum

2. Problem Statement
• Use of Back Propagation Algorithm for Feed Forward Neural Networks
• Local Optima
• Trivial Manual Intervention
• Time Consumption

3. Proposal
TFFN
Uses Concept of ELM
Has Better Generalization
Capability
Optimizes the Hyper-
parameters

4. What is ELM?
• Extreme Learning Machines
• Used for Single Hidden Layer Feed Forward Networks
• Uses the Hidden Layer Output matrix Concept and
Generalized Pseudo Inverse
Hidden Layer Output Matrix
ELM-SLFN
𝑓𝐿 𝑥 =
𝑖=1
𝐿
β𝑖ℎ𝑖 𝑥 = ℎ 𝑥 β
where ,
β = β1, β2, … , β 𝐿
𝑇
, 𝑜𝑢𝑡𝑝𝑢𝑡 𝑤𝑒𝑖𝑔ℎ𝑡 𝑚𝑎𝑡𝑟𝑖𝑥
and
h(x)= [ℎ1 𝑥 , ℎ2 𝑥 , … , ℎ 𝐿 𝑥 ] , ℎ𝑖𝑑𝑑𝑒𝑛 𝑙𝑎𝑦𝑒𝑟 𝑜𝑢𝑡𝑝𝑢𝑡
𝑚𝑎𝑡𝑟𝑖𝑥
Generalized Pseudo Inverse
If the above problem is:
Ax = b
x = 𝑨ᵻ
𝒃

5. Results
Accuracy Comparison-Glass Accuracy Comparison-Diabetes
ELM-SLFN Vs TFFN

6. TFFN: Two Hidden Layer Feed Forward Network
Computation of Hidden
layer output matrix
Calculation of β

7. TFFN: Mathematical Formulation
Two Hidden Layer Linear Formulation
When closely seen, the above equation:
Target
𝑊𝑖 𝑊𝑗

8. TFFN: Theorems behind it
• Universal approximation capability
• Classification Capability Theorem
• Moore Penrose Generalized inverse
• Least Squares Solution

9. Conclusions
• The randomness concept avoids the term “Iterations” as in case of Back
Propagation
• It has a better “Generalization Capability” than Back Propagation
• This automatically optimizes the Hyperparameters
• The randomness can scrutinize the algorithm to “Move in one direction only”
• The algorithm can’t assign priority on the basis of the training dataset

10. References
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