Let’s get started! The underlying intuition I hope you are excited as I am about this project. Keep in mind that this is my approach - if you like the overall reasoning, but you’d do something different, please let me know with a comment 👌 As I keep on working on this project, I will share my findings in another article. I have put some hours into this project, and I would like to share with you the first part of this series. This would allow me to spot relevant sequences that could predict a specific trend in a probabilistic fashion. What if I could identify patterns in data, find similar patterns, group them together and assign investment opportunities to each pattern? I am talking about time series clustering applied to stock markets. Today I want to propose a different approach, one that I literally dreamt about a couple of weeks ago and that kept me thinking because of the unexpected opportunities. That’s not a bad thing, as analysts can initiate their projects following a shared baseline and then expand with their own methodologies.

These approaches are definitely interesting, and for a good reason - there are plenty of resources on the internet that deals with the topics mentioned.

The most relevant ones today mostly use neural networks applied to sequence data, such as LSTMs, or involve sentiment analysis and signal detection. If you are an analyst, regardless of your job sector, you are definitely aware that there are many different approaches to stock market prediction.

Time series clustering