"Pronóstico de Series Temporales usando inferencia Bayesiana: aplicación a series de lluvia de agua acumulada"
(Time Series Forecasting Using Bayesian Method: Application to Cumulative Rainfall)
Cristian Rodríguez Rivero (email@example.com), Julian Pucheta (firstname.lastname@example.org), Martín Herrera (email@example.com), Victor Sauchelli (firstname.lastname@example.org), Sergio Laboret (email@example.com)
This paper appears in: Revista IEEE América Latina
Publication Date: Feb. 2013
Volume: 11, Issue: 1
In this work an algorithm to adjust parameters
using a Bayesian method for cumulative rainfall time series
forecasting implemented by an ANN-filter is presented. The
criterion of adjustment comprises to generate a posterior
probability distribution of time series values from forecasted time
series, where the structure is changed by considering a Bayesian
inference. These are approximated by the ANN based predictor in
which a new input is taken in order for changing the structure
and parameters of the filter. The proposed technique is based on
the prior distribution assumptions. Predictions are obtained by
weighting up all possible models and parameter values according
to their posterior distribution. Furthermore, if the time series is
smooth or rough, the fitting algorithm can be changed to suit, in
function of the long or short term stochastic dependence of the
time series, an on-line heuristic law to set the training process,
modify the NN topology, change the number of patterns and
iterations in addition to the Bayesian inference in accordance
with Hurst parameter H taking into account that the series
forecasted has the same H as the real time series.
The performance of the approach is tested over a time series
obtained from samples of the Mackey-Glass delay differential
equations and cumulative rainfall time series from some
geographical points of Cordoba, Argentina.
Bayesian approach, neural networks, time series forecast, Hurst’s parameter.
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