A comparison study of outpatient visits forecasting effect between ARIMA with seasonal index and SARIMA

Zhang, Xinxiang; Zhou, Bo; Fu, Huijuan

doi:10.1109/pic.2017.8359573

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…. , q Q ; D S signifies the seasonal difference, which goes through the D order (45). With respect to model order p, q and P, Q, the approach of how to get the approximate values is the same as ARIMA.…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Xiao

Xue

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Container shipping has suffered a sharp decline since COVID-19, and risks associated with container transit will persist in the future. The decrease in container transportation has caused a ripple impact on the global supply chain. However, container throughput forecasting is both critical and complicated under the circumstances of economic uncertainty and the outbreak of the COVID-19 pandemic. A novel model propounded in this paper for container throughput forecasting to assist the port management bureau and container shipping industry integrates with the variational mode decomposition (VMD) algorithm, SARIMA technique, convolutional neural network (CNN) method, long short-term memory (LSTM) approach, and attention mechanism, among others. In this model, there are three stages: (i) data decomposition, (ii) component prediction, and (iii) ensemble output. In the first stage, the original data of the container throughput time series is decomposed into several different components using the VMD algorithm. Next, from low frequency to high frequency, each component is modeled by the corresponding prediction approach. Subsequently, the prediction results of each component generated by the previous stage are integrated into the final forecasting results by addition strategy. To enhance the prediction accuracy in the second stage, the attention mechanism is adopted in the CNN-bidirectional LSTM method. Finally, six measurement criteria, the container throughput times series at four ports, and a statistical evaluation approach are applied to comprehensively evaluate the proposed model compared with seven benchmark models. The empirical analysis demonstrates that the proposed model significantly outperforms other comparable models with regard to prediction results, level, and directional prediction accuracy.

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Section: Variational Mode Decompositionmentioning

confidence: 99%

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Xiao

Xue

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…, , , , S SARIMA p d q P D Q . where p and P represent the order of autoregression and seasonal autoregression, respectively; and d and D are permuted and combined according to different differences and differences in different seasons; q and Q are the order of moving averages and seasonal moving averages, respectively [11] . Its formula is as follows:…”

Section:   mentioning

confidence: 99%

Short-term Passenger Flow Prediction of railway epidemic Based on SARIMA - LSTM combined model

WEI

Zhang

2022

Preprint

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The huge disturbance caused by the sudden outbreak of COVID-19 to the short-term passenger flow of railway. The daily passenger flow curves of periodic and seasonal non-stationary time series of Spring Festival travel under the COVID-19 pandemic were analyzed, and the combined model based on SARIMA-LTSM was constructed. SARIMA model was used to predict the linear part and LSTM rolling optimization model was used to predict the nonlinear part. Finally, the two prediction results were put into the weighted sum of attention mechanism module, and the GRU gated loop unit was introduced to assist verification. Through case study and analysis, the results show that the prediction results of SARIMA-LTSM combined model have good control and high accuracy, which can provide theoretical basis for the close representation and prediction of short-term passenger flow data set of epidemic emergencies.

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“…Q is the order of the seasonal moving-average (SMA) process. S is the length of the seasonal cycle (Xinxiang, Zhou, and Huijuan 2017). SARIMA model's formula is as follows:…”

Section: Model Identification and Estimationmentioning

confidence: 99%

“…. ; # Q acts as seasonal mobbing average parameter; Ñ D S signifies the seasonal difference which goes through the D order (Xinxiang, Zhou, and Huijuan 2017).…”

Section: Model Identification and Estimationmentioning

confidence: 99%

Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models

Koyuncu

Tavacıoğlu

Gökmen

et al. 2021

Maritime Policy & Management

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Maritime operators are facing their biggest challenge called Coronavirus (COVID-19) since the 2008 financial crisis. As part of the measures taken by the countries against the virus, the domino effect started with the breaks in the interconnected supply chain, like the spider web. The main purpose of this study is modeling the Institute of Shipping Economics and Logistics (ISL) and the Leibniz-Institut für Wirtschaftsforschung (RWI) Container Throughput Index with the time series, to reveal the relationship between the short-term forecast results and the COVID-19 seen in the first months of 2020. The deep effect of COVID-19 on maritime trade is investigated by forecasting the RWI/ISL Container Throughput Index in 89 major international container ports, including and excluding seasonal variations. The modeling process of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing State Space Model (ETS) is explained. To evaluate SARIMA and ETS models' performance, information criteria, and error measurements are calculated and compared. SARIMA model is found as more suitable model than ETS forecasting seasonally and working-day adjusted and original RWI/ISL. The results indicated that the SARIMA model is suitable and efficient for the forecasting of RWI/ISL. Three months' forecasting results are showed that the decrease will continue.

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A comparison study of outpatient visits forecasting effect between ARIMA with seasonal index and SARIMA

Cited by 11 publications

References 9 publications

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Short-term Passenger Flow Prediction of railway epidemic Based on SARIMA - LSTM combined model

Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models

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