How to Filter Irregularly Spaced Data

One of the more challenging tasks to perform in an analytics or data science role is to find the best way to filter irregularly spaced data. In this article we will cover the ol’ fashioned manual method as well as a software based solution. If you are working with a massive dataset, consider using a definition query to get the lowdown on your data. In the event that you are dealing with a single point cloud, you may opt for a bilateral filter.

To be honest, there is no magic pill when it comes to filtering irregularly spaced data. You can use software tools to transform your data into a smooth and uniform grid but the results can be mixed up. This is especially true if you are relying on a resampler to do the heavy lifting for you. To ensure that you are making the most informed decision, you need to understand the nuances of your data and its limitations.

Luckily, there is a plethora of software tools to choose from. In particular, the Traces Python library offers a myriad of methods for analyzing irregularly spaced time series. Aside from the obvious methods like interpolation and resampling, you can also try out a handful of statistical methods. Among these are the use of a LinearTriInterpolator and manual means of reorganizing your data. The next step is to identify the patterns in your data and to identify the most important ones. Once you are done with that, you can finally start analyzing the data in an informed manner. If you are unsure of how to filter irregularly spaced data, enlist the aid of a data scientist. This will ensure that you are not only able to make the most informed decision possible but also to avoid making any missteps along the way. Hopefully this article has served as a guide to how to filter irregularly spaced data in a safe and informed fashion. The results will be worth the wait.