Time Series Complexity analysis using Entropy
<p>Every data scientist knows this:<strong> the first step to the solution of a Machine Learning problem is the exploration of the data.</strong></p>
<p>And it’s not only about understanding which features can help you in solving the problem. That is actually something that requires domain knowledge, a lot of effort, a lot of asking around and trying to find out. That is a necessary step, but in my opinion, is step number <strong>two</strong>.</p>
<p>The first step is in some way, shape, or form, based on the analysis of how <strong>complex</strong> your data is. Are they asking you to find fine details and pattern in something that is kind of always the same, or the outputs are completely different from each other? Do they want you to find the distance between 0.0001 and 0.0002 or do they want you to find the distance between 0 and 10?</p>
<p><a href="https://towardsdatascience.com/time-series-complexity-analysis-using-entropy-ec49a4aaff11"><strong>Website</strong></a></p>