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Haskell Data Analysis cookbook

You're reading from   Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783286331
Length 334 pages
Edition 1st Edition
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Author (1):
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Nishant Shukla Nishant Shukla
Author Profile Icon Nishant Shukla
Nishant Shukla
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Table of Contents (14) Chapters Close

Preface 1. The Hunt for Data FREE CHAPTER 2. Integrity and Inspection 3. The Science of Words 4. Data Hashing 5. The Dance with Trees 6. Graph Fundamentals 7. Statistics and Analysis 8. Clustering and Classification 9. Parallel and Concurrent Design 10. Real-time Data 11. Visualizing Data 12. Exporting and Presenting Index

Calculating a moving average


Summarizing a list of numbers into one representative number can be done by calculating the average. The equation for the arithmetic mean is to add up all the values and divide by the number of values. However, if the values being summed over are extremely large, the total sum may overflow.

In Haskell, the range for Int is at least from -2^29 to 2^29-1. Implementations are allowed to have an Int type with a larger range. If we try to naively average the numbers 2^29-2 and 2^29-3 by first calculating their sum, the sum may overflow, producing an incorrect calculation for the average.

A moving average (or running average) tries to escape this drawback. We will use an exponential smoothing strategy, which means numbers that were seen previously contribute exponentially less to the value of the running mean. An exponential moving average reacts faster to recent data. It can be used in situations for detecting price oscillations or spiking a neuron in a neural network...

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