Multivariate High Order Fuzzy Time Series Forecasting For Car Road Accidents

Jilani, Tahseen Ahmed; Burney, S. M. Aqil; Ardil, Cemal

doi:10.5281/zenodo.1085455

Cited by 26 publications

(1 citation statement)

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“…This approach is typically used as a first step in preparing the universe of speech for numerical evaluation by tying events from different time periods together. Differences in time series data have been employed as the universe of discourse in several forecasting systems [46]. Time series data differences can improve forecasting accuracy.…”

Section: Research Methods 21 Fuzzy Time Series: a Basic Conceptmentioning

confidence: 99%

Prediction of passenger train using fuzzy time series and percentage change methods

Solikhin¹,

Lutfi²,

Purnomo

et al. 2021

Bulletin EEI

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In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

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Section: Research Methods 21 Fuzzy Time Series: a Basic Conceptmentioning

confidence: 99%