AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Splunk data analytics9/26/2023 Determining adaptive thresholds from historic data that apply at different times or for different sources to your data.Determining thresholds based on historic data that apply uniformly to your data or.Time series analysisīroadly there are a couple of ways of detecting anomalies in Splunk, either: While the spectrum of EDA is broad - and we cover some approaches in our MLTK documentation - I thought I would walk through a few simple techniques that can be used to understand how to prepare your data for anomaly detection.Įssentially, we are going to take you through two examples of how to profile your data here using time series analysis and using histograms to understand how the data is distributed. EDA is a wonderful catch-all term for the wide variety of analysis you can perform to figure out what comprises your data and what patterns exist within it. The simplest answer to this question is one of the dark arts of data science: Exploratory Data Analysis (EDA). With great choice comes great responsibility, however, and one of the most frequent questions I encounter when speaking to customers about anomaly detection is how do I choose the best approach for identifying anomalies in my data? Perhaps you even have a handful of analytics running in Splunk to identify anomalies in your data using statistical models, using probability density functions or even using clustering. ![]() Many of you might have seen our blogs about different techniques for identifying anomalies.
0 Comments
Read More
Leave a Reply. |