Abstract
In this paper, we present a neural network based system to generate an adaptive model for financial time series tracking. This kind of data is quite relevant for data quality monitoring in large databases. The proposed system uses the past samples of the series to indicate its future trend and to generate a corridor inside which the future samples should lie. This corridor is derived from an adaptive forecasting model, which makes use of the walk-forward method to take into account the most recent observations of the series and bring up to date the values of the neural model parameters. The model can serve also to manage other time series characteristics, such as the detection of irregularities.
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Dantas, A.C.H., Seixas, J.M. (2005). An Adaptive Neural System for Financial Time Series Tracking. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_101
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DOI: https://doi.org/10.1007/3-211-27389-1_101
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-24934-5
Online ISBN: 978-3-211-27389-0
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