The amount of information we are exposed to on a daily basis is far outstripping our ability to consume it, leaving many of us overwhelmed by the amount of new content we have available. Ideally we’d like machines and algorithms to help us find the more interesting (for us individually) things so we more easily focus our attention on items of relevance.
This is the final piece to a three part blog series. If you would like to view the previous parts to this series please use the following link:
Previously I explained how to use Excel and R as the analysis tools to calculate the Simple Moving Average of a small set of stock closing prices.
This is the second post of a three part blog series. If you would like to read Part 1, please follow this link. In this post we will be reviewing a simple moving average in contexts that should be familiar to the analyst not well versed in Hadoop as to establish a common ground with the reader from which we can move forward.
A Quick Primer on Simple Moving Average in Excel
Let’s take a second to do a quick review of how we define simple moving average in an Excel spreadsheet.
In this three part blog series I want to take a look at how we would do a Simple Moving Average with MapReduce and Apache Hadoop. This series is meant to show how to translate a common Excel or R function into MapReduce java code with accompanying working code and data to play with. Most analysts can take a few months of stock data and produce an excel spreadsheet that shows a moving average,