Abstract Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic model. The built using intelligent data provided large-scale war...

Firms use time-series forecasting methods to predict sales. However, it is still a question which method forecaster best, if only single forecast needed. This study investigates and evaluates different sales methods: multiplicative Holt-Winters (HW), additive HW, seasonal auto regressive integrated moving average (SARIMA) [a variant of (ARIMA)], long short-term memory (LSTM) recurrent neural ne...

To achieve greater sustainability of the traffic system, trend accidents in road was analysed. Injuries from are among leading factors suffering people around world. predicted to be third factor contributing human deaths. Road have decreased most countries during last decade because Decade Action for Safety 2011–2020. The main reasons behind reduction improvements construction vehicles and road...

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA technique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are compared with those obtained using Box-Jenkins ...

This paper aims to determine suitable seasonal autoregressive integrated moving average (SARIMA) and feed-forward neural network (FFNN) models forecast the total non-coincidental monthly system peak demand in Philippines. To satisfy stationary requirement of SARIMA model, differencing, first-differencing were applied. The findings reveal that (0,1,1)(0,1,1)12 is appropriate model. All model par...

Multiple seasonal patterns, which often interact with each other, play a key role in time series forecasting, especially for business where effects are dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of period patterns. We propose mixed hierarchical seas...

In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a k-factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price market for the period August 15, 2000 December 31, 2002 and we test spot prices forecasts until ...