Abstract
This paper presents two approaches to symbolic interval time series forecasting. The first approach is based on the autoregressive moving average (ARMA) model and the second is based on a hybrid methodology that combines both ARMA and artificial neural network (ANN) models. In the proposed approaches, two models are respectively fitted to the mid-point and range of the interval values assumed by the symbolic interval time series in the learning set. The forecast of the lower and upper bounds of the interval value of the time series is accomplished through the combination of forecasts from the mid-point and range of the interval values. The evaluation of the proposed models is based on the estimation of the average behaviour of the mean absolute error and mean square error in the framework of a Monte Carlo experiment.
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© 2006 Springer-Verlag Berlin Heidelberg
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Maia, A.L.S., de A.T. de Carvalho, F., Ludermir, T.B. (2006). A Hybrid Model for Symbolic Interval Time Series Forecasting. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_103
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DOI: https://doi.org/10.1007/11893257_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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