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Master-Thesis-main.zip (12 MB)
tfm_victoria_galindo_ana.pdf (4.65 MB)

Document type

Master thesis

Publication date

2024-06-28

Publication license

cc-by-nc-nd (c) Ana Victoria Galindo, 2024
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/215450

Application of the signature method in time series and financial data streams

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Abstract

This thesis focuses into the signature method’s role as a robust tool in data science, specifically within the realms of time series analysis and financial data streams. Originating from rough paths theory, the signature method offers a comprehensive representation of sequential data, effectively capturing intricate patterns and dependencies crucial for advanced modeling and predictive analytics. Establishing a solid theoretical foundation, this thesis explores how the signature method transforms raw time series data into structured representations that preserve essential dynamic information. Through theoretical insights and practical illustrations, the thesis demonstrates the method’s efficacy in enhancing model classification, temporal segmentation, and understanding complex model structures.

Description

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2023-2024. Tutor: Josep Vives i Santa Eulàlia

Subject

Anàlisi de sèries temporals, Aprenentatge automàtic, Anàlisi de regressió, Treballs de fi de màster

Subject (English)

Time-series analysis, Machine learning, Regression analysis, Master's thesis

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Màster Oficial - Fonaments de la Ciència de Dades
Programari - Treballs de l'alumnat

Citation

VICTORIA GALINDO, Ana. Application of the signature method in time series and financial data streams. [consulted: 12 of August of 2026]. Available at: https://hdl.handle.net/2445/215450

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