Abstract
According to the of time series of faults in railway point machines (RPMs), forecasting approach based on the generalised linear autoregressive moving average (GLARMA) models and the integer-valued generalised autoregressive conditional heteroscedastic (INGARCH) models are presented. The conditional distribution of observed fault counts of given previous faults and weather conditions are assumed to be Poisson or negative binomial distributions. The forecasting future fault counts of RPM are obtained by one-step-ahead forecasts and the performance evaluation shows that the GLARMA method performs better than the traditional autoregressive moving-average (ARMA) model and generalised linear model (GLM).
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