Machine learning in trading: theory, models, practice and algo-trading - page 3107
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Interesting read
https://quant.stackexchange.com/questions/1004/how-are-cryptography-and-speech-recognition-technology-applied-to-forecasting-fi
Yeah and in general, the whole[quant-trading-strategies] tag isvery interesting to go through
A present in the hope of a review
Which clustering method is best suited for grouping such objects?
Basically there is a matrix, and it is important to evaluate its similarity as a whole. And for some reason, K-means, I think, will average everything out a lot.
Chat-GPT:
"
If matrices are descriptions of a three-dimensional object, then clustering methods that take into account the structure of three-dimensional data can be used to group them. Here are a few approaches that may be useful:
Density-Based Clustering Method: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a good choice because it takes into account the density of the data. You can apply DBSCAN to three-dimensional matrices using proximity measures or distances between points in three-dimensional space. This method can help you identify clusters that form compact and dense regions in three-dimensional space.
Spectral clustering: The spectral clustering method can be useful for clustering three-dimensional matrices. You can apply a spectral transform to three-dimensional data and then use clustering methods (e.g., k-means) to separate into clusters. This approach allows you to take into account the structure of the data and identify groups that are similar in three-dimensional space.
Hierarchical clustering: Hierarchical clustering can be applied to three-dimensional matrices to build a hierarchical structure of clusters. You can use similarity or distance measures between matrices and merge or separate clusters based on these measures. This approach will help you explore hierarchical relationships between groups of three-dimensional matrices.
It is also important to consider the characteristics of your particular dataset and choose the clustering method that best suits your goals and requirements. Experiment with different methods and parameters to find the most appropriate approach for your task.
"
Chat-GPT:
I myself thought to make a convolution of the matrix on 5 points, through the averaging of neighbouring points, and on them already search for similarity by some method.
And for some reason, K-means is going to be averaging things out a lot.
I was thinking of convolution of the matrix by 5 points, through averaging of neighbouring points, and using them to search for similarity by some method.
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the flask whistles ))
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the flask whistles.)
Strong - one centroid. and I was thinking of actually several with fixed coordinates..... However, who am I talking to - a lover of off-the-shelf solutions.....
A present in the hope of a review
Which clustering method is best suited for grouping such objects?
Basically there is a matrix, and it is important to evaluate its similarity as a whole. And for some reason, K-means seems like it would average things out a lot.
h ttps:// habr.com/ru/companies/jetinfosystems/articles/467745/
Didn't see an answer to the question in the link.
I didn't see a question answered in the link.