@inproceedings{84949124234,
    abstract = "© Springer International Publishing Switzerland 2014.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.",
    year = "2014",
    title = "A machine learning method for high-frequency data forecasting",
    volume = "8827",
    keywords = "ACM-ACD model , Financial high-frequency data , Forecasting , Machine learning , Time series",
    pages = "621-628",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    author = "Allende, Héctor and López, Erick and Allende-Cid, Héctor"
}

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