Missing data in time series analysis —Does this sound familiar to you?
Are you all too familiar with missing data in your data sets due to malfunctioning sensors, transmission, or any type of maintenance?
Well, missing values derail your forecast and distort your analysis.
So how are they solved?
Traditional methods may seem like the solution: direct fill or interpolation: but is that good enough??
What happens when your data has complex patterns, non-linear trends, or high variability? Simple techniques would fail and produce unstable results.
What if there were wiser ways to meet this challenge?
Machine learning does just that: from regression analysis to K-Nearest Neighbors to neural networks, which involve nothing more than adapting and filling in the gaps with precision.
Curious? Let's take a deeper look at how those advanced methods will change your time series analysis.
We will attribute missing data by using a data set that you can easily generate yourself…
