A Study on Demand Forecasting for KTX Passengers by using Time Series Models

Kim, In Joo; Sohn, Hueng-Goo; Kim, Sahm

doi:10.5351/kjas.2014.27.7.1257

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…( 1). RNN suffer from the gradient vanishing problem during backpropagation, leading to the issue of long-term dependencies [12], [19].…”

Section: A Theoretical Background Of Recurrent Neural Networkmentioning

confidence: 99%

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Study on Stochastic and Autoregressive Time Series Forecasting for Hydrogen Refueling Station

Kim,

Won,

Park

et al. 2023

IEEE Access

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Hydrogen refueling stations are pivotal for renewable energy and carbon neutrality; however, they encounter challenges owing to equipment malfunctions. This study addresses the use of time-series forecasting techniques to predict and diagnose critical equipment failure at these stations. An analysis of the station equipment was conducted to create scenarios for potential malfunctions in compression equipment. Techniques such as the Recurrent Neural Network(RNN), Long Short-Term Memory network (LSTM), and Gated Recurrent Unit(GRU) have been employed to forecast the conditions of high-pressure compression equipment. Deep neural networks were constructed to enhance prediction accuracy, typically achieving an error margin of 0.01. Multi-step predictions using autoregression were utilized to bolster equipment resilience against aging and progressive failures. Autoregressive prediction models, particularly those using LSTMs and GRUs, outperform RNNs. However, predictions may be subject to errors due to algorithmic limitations and environmental factors. This study introduces a stochastic forecasting approach that, utilizes Gaussian distributions to predict probability distributions, not single-point estimates. This method yielded a 95% prediction interval with a standard deviation of 1.96. The reliability of multi-time step forecasts is significantly improved by adopting stochastic autoregressive forecasting and establishing prediction intervals. The proposed model enhances not only the accuracy of equipment failure predictions but also proactive maintenance, thus reducing downtime and boosting the efficiency of the hydrogen fuel infrastructure, which contributes to the wider utilization of hydrogen as a clean energy source.

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“…( 1). RNN suffer from the gradient vanishing problem during backpropagation, leading to the issue of long-term dependencies [12], [19].…”

Section: A Theoretical Background Of Recurrent Neural Networkmentioning

confidence: 99%

“…The update gate determines candidate information to be added to the cell state. The cell state determines the memory passed to the next time step [13], [19]- [21].…”

Section: Figure 3 Forward Propagation For Rnnmentioning

confidence: 99%

Study on Stochastic and Autoregressive Time Series Forecasting for Hydrogen Refueling Station

Kim,

Won,

Park

et al. 2023

IEEE Access

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The impacts of high speed train on the regional economy of Korea

Park

Kim²

2016

Korean Journal of Applied Statistics

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High-speed railway (Korea Train Express) has had a deep impact on the regional economy of Korea. Current high-speed rail research is mostly theoretical, there is a lack of quantitative research using a precise algorithm to study the effect of high-speed railway on the regional economy. This paper analyses the influence of highspeed rail on the regional economy, with a focus on the Daegu area. Quantitative analysis using department store indexes and regional medical records is performed to calculate the economic influence of high-speed rail. The result shows that high-speed railway effects the regional economy though regional consumption growth and medical care trends.

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A Study on Demand Forecasting for KTX Passengers by using Time Series Models

Cited by 2 publications

References 6 publications

Study on Stochastic and Autoregressive Time Series Forecasting for Hydrogen Refueling Station

Study on Stochastic and Autoregressive Time Series Forecasting for Hydrogen Refueling Station

The impacts of high speed train on the regional economy of Korea

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