A machine learning method for high-frequency data forecasting

Erick López, Héctor Allende, Héctor Allende-Cid

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

In recent years several models for financial high-frequency data have been proposed. One of the most known models for this type of applications is the ACM-ACD model. This model focuses on modelling the underlying joint distribution of both duration and price changes between consecutive transactions. However this model imposes distributional assumptions and its number of parameters increases rapidly (producing a complex and slow adjustment process). Therefore, we propose using two machine learning models, that will work sequentially, based on the ACM-ACD model. The results show a comparable performance, achieving a better performance in some cases. Also the proposal achieves a significatively more rapid convergence. The proposal is validated with a well-known financial data set.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings
EditoresEduardo Bayro-Corrochano, Edwin Hancock
EditorialSpringer Verlag
Páginas621-628
Número de páginas8
ISBN (versión digital)9783319125671
DOI
EstadoPublicada - 2014
Publicado de forma externa
Evento19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 - Puerto Vallarta, México
Duración: 2 nov. 20145 nov. 2014

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8827
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
País/TerritorioMéxico
CiudadPuerto Vallarta
Período2/11/145/11/14

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