Tidy Time Series Analysis, Part 2: Rolling Functions
Tour the Interface Types of Steps. You can see which functions are integrated into tidyquant package below:. Now you have been given the following data in which some points are circled red that are representing support vectors. One way to do this is to use moving averages.
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Want to know how to begin? This is the video for you. Tour the Interface Types of Steps. Editing Metadata Editing Data Connections. Storytelling with tooltips Tooltip selection Command Buttons Actions from tooltips Conditional tooltips. Ambiguous Geographic Data Editing Locations.
String Parameters Splitting Strings. One-to-many relationships Joins inflating the number of rows Benefits of each. What are Bollinger Bands? What is a Bump Chart? Building a Bump Chart. What is a Control Chart? What is a Funnel Chart? Line Graphs Step and Jump Lines. What is a Pareto Chart? What is a Waterfall Chart? The last point is this is only a six month window of data.
You may find in your analytic endeavors that you want more than one statistic. The custom function can then be applied in the same way that mean was applied.
Now for the fun part: The process is almost identical to the process of applying mean with the main exception that we need to set by. We can see periods of consolidation and periods of high variability. Many of the high variability periods are when the package downloads are rapidly increasing. The rollapply functions from zoo and TTR can be used to apply rolling window calculations. We were able to use the rollapply functions to visualize averages and standard deviations on a rolling basis, which gave us a better perspective of the dynamic trends.
Using custom functions, we are unlimited to the statistics we can apply to rolling windows. In fact, rolling correlations, regressions, and more complicated statistics can be applied, which will be the subject of the next posts.
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