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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

Time series decomposition

One of the problems that we have modeling the military time series is that there is simply not enough data to be able to produce a general model of the process that produced the series. A common way to model a time series is to decompose the series into a number of separate components:

  • Trend: Does the series generally increase or decrease over time? Is the trend an exponential curve as we saw with the population?
  • Seasonality: Does the series exhibit periodic rises and falls at a set number of intervals? For monthly data it is common to observe a period cycle of 12 months.
  • Cycles: Are there longer-term cycles in the dataset that span multiple seasons? For example, in financial data we might observe multi-year cycles corresponding to periods of expansion and recession.

Another way of specifying the issue with the military data is that there is not enough information to determine whether or not there is a trend, and whether the observed peak is part of a seasonal or cyclic...

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