Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series

Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series

While Machine Learning significantly boosts the performance of predictive models, its efficacy varies across different data dimensions. It is essential to cluster time series data of similar characteristics, particularly in the financial sector. However, clustering financial time series data poses c...

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Bibliographic Details
Main Authors: Daniel Gonzalez Cortes, Enrique Onieva, Iker Pastor Lopez, Laura Trinchera, Jian Wu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Clustering methods
data compression
financial data processing
neural network applications
time series
Online Access:https://ieeexplore.ieee.org/document/10415421/

Internet

https://ieeexplore.ieee.org/document/10415421/

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