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Hands-On Neural Networks with Keras

You're reading from  Hands-On Neural Networks with Keras

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789536089
Pages 462 pages
Edition 1st Edition
Languages
Author (1):
Niloy Purkait Niloy Purkait
Profile icon Niloy Purkait

Table of Contents (16) Chapters

Preface 1. Section 1: Fundamentals of Neural Networks
2. Overview of Neural Networks 3. A Deeper Dive into Neural Networks 4. Signal Processing - Data Analysis with Neural Networks 5. Section 2: Advanced Neural Network Architectures
6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Long Short-Term Memory Networks 9. Reinforcement Learning with Deep Q-Networks 10. Section 3: Hybrid Model Architecture
11. Autoencoders 12. Generative Networks 13. Section 4: Road Ahead
14. Contemplating Present and Future Developments 15. Other Books You May Enjoy

Implementing exponential smoothing

So, we transform our training data by looping over each mid-price value, updating the smoothing coefficient, and then applying it to the current price value. Note that we update the smoothing coefficient using the previously shown formula, which allows us to weight each observation in the time series as a function weighting the current and previous observations:

Smoothing = 0.0     #Initialize smoothing value as zero

gamma = 0.1         #Define decay

for i in range(1000):
      
    Smoothing = gamma*train_data[i] + (1-gamma)*Smoothing   # Update   
smoothing value
train_data[i] = Smoothing # Replace datapoint with smoothened value

Visualizing the curve

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