Part 1: Leverage linear regression and decision trees to impute time-series gaps.
Missing data in time-series analysis — sounds familiar?
Does missing data in your datasets due to malfunctioning sensors, transmission, or any kind of maintenance sound all too familiar to you?
Well, missing values derail your forecast and skew your analysis.
So, how do you fix them?
Traditional methods may seem like the solution-forward fill or interpolation — but is that good enough?
What happens when your data has complex patterns, nonlinear trends, or high variability? Simple techniques would fail and render unstable results.
What if there were wiser ways to face this challenge?
Machine learning does just that: from regression analysis through K-Nearest Neighbors to neural networks, which do not assume anything but adapt and fill in the gaps with precision.
Curious? Let’s look deeper at how those advanced methods will change your time-series analysis.
We will impute missing data in using a dataset that you can easily generate yourself…
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