Calculating moving averages with KQL
There may be instances where your time series data is clean and all the components such as seasonality, trends, and variations are visible to the point that you can confidently make decisions based on the data without having to manipulate or clean the data. In reality, there will be noise and variations in the data that may obfuscate patterns and anomalies. KQL provides a rich set of functions for analyzing time series data. One subset of those functions is for calculating moving averages. Moving averages allow us to remove noise and smoothen our data.
The goal of this section is to learn how to use series_fir()
to calculate moving averages to smoothen our data. Finite Impulse Response (FIR) is a filtering technique that is commonly used in signal processing and time series.
As you may recall, in Chapter 6, Introducing Time Series Analysis, we used demo_make_series1
, which is a table in the help cluster (https://help.kusto.windows.net) to...