@inproceedings{aa386f8e3c27464cbc1a304b55be4ecc,
title = "A machine learning method for high-frequency data forecasting",
abstract = "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.",
keywords = "ACM-ACD model, Financial high-frequency data, Forecasting, Machine learning, Time series",
author = "Erick L{\'o}pez and H{\'e}ctor Allende and H{\'e}ctor Allende-Cid",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 ; Conference date: 02-11-2014 Through 05-11-2014",
year = "2014",
doi = "10.1007/978-3-319-12568-8_76",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "621--628",
editor = "Eduardo Bayro-Corrochano and Edwin Hancock",
booktitle = "Progress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings",
}