Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Modelling of fault in RPM using the GLARMA and INGARCH model
- Authors
- Kim, Ji-Yong; Kim, Hee-Young; Park, Daihee; Chung, Yongwha
- Issue Date
- 8-Mar-2018
- Publisher
- INST ENGINEERING TECHNOLOGY-IET
- Keywords
- railways; time series; fault diagnosis; forecasting theory; autoregressive moving average processes; Poisson distribution; binomial distribution; generalised linear model; autoregressive moving-average model; performance evaluation; negative binomial distribution; Poisson distribution; integer-valued generalised autoregressive conditional heteroscedastic model; generalised linear autoregressive moving average model; forecasting approach; railway point machine; time series; fault modeling; GLARMA model; INGARCH model; RPM
- Citation
- ELECTRONICS LETTERS, v.54, no.5
- Indexed
- SCIE
SCOPUS
- Journal Title
- ELECTRONICS LETTERS
- Volume
- 54
- Number
- 5
- ISSN
- 0013-5194
- 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).
- Files in This Item
- There are no files associated with this item.
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- Kim, Hee Young
- College of Public Policy (Division of Big Data Science)