One of the common problems in Time series analysis is missing data.
As we have seen in Part 1, simple imputation techniques or regression-based models such as linear regression and decision trees can help us a lot.
But what if we need to drive more? subtle patterns and capture fine fluctuations in complex time series data?
In this article, we will explore how a Neural network (NN) can be used to impute missing values.
The strengths of NN are their Ability to capture non-linear patterns and interactions in data. Although NNs are typically computationally expensive, they can offer a very efficient way to impute missing time series data in cases where simpler models fail.
We will work with the same data set as in Part 1 and Part 2, 10% missing values, entered randomly for the simulated energy production data set.
Don't miss it Part 1 from this series:
