• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.653-663
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• 2021

There has been a massive increase in household debt in China, especially in the last five of years. Learning from past experiences, the country needs careful forecasting that may help to form new policies or make amendments to the existing ones. This research paper aims to highlight the impact of the monetary policy on household debt in China. The study covers the time period from 1996 to 2020 The study employs a cointegration test, Autoregressive Distributed Lag Bound Test (ARDL) approach, a Augmented Dicky Fuller (ADF) and PP test (PMG) and time series data. The findings suggest on a quantitative analysis using a time-series model in which gdp per capita and interest rate has a positive impact on household debt whereas, cpi doesn't have significant impact. In a short-term variables relationship, household debt responds more to an increase in income than in the long-term. Also, the impact of interest rate changes on household debt is lower than income in the short run.The research suggests that there should be some restrictions on household debt and consumer financing provided to citizens and for this, appropriate leverage measures should be taken in order for the central bank to sustain robust macroeconomic conditions.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.295-302
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• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.4
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• pp.382-391
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• 2020

In this study, we used artificial intelligence algorithms for the prediction of dissolved oxygen in Jindong Bay. To determine missing values in the observational data, we used the Bidirectional Recurrent Imputation for Time Series (BRITS) deep learning algorithm, Auto-Regressive Integrated Moving Average (ARIMA), a widely used time series analysis method, and the Long Short-Term Memory (LSTM) deep learning method were used to predict the dissolved oxygen. We also compared accuracy of ARIMA and LSTM. The missing values were determined with high accuracy by BRITS in the surface layer; however, the accuracy was low in the lower layers. The accuracy of BRITS was unstable due to the experimental conditions in the middle layer. In the middle and bottom layers, the LSTM model showed higher accuracy than the ARIMA model, whereas the ARIMA model showed superior performance in the surface layer.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.50-53
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• 2017

Not only growth of importance to understanding economic trends, but also the prediction to overcome the uncertainty is coming up for long-term maritime recession. This paper discussed about the prediction of BDI with artificial neural networks (ANN). ANN is one of emerging applications that can be the finest solution to the knotty problems that may not easy to achieve by humankind. Proposed a prediction by implementing neural networks that have recurrent architecture which are a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). And for the reason of comparison, trained Multi Layer Perceptron (MLP) from 2009.04.01 to 2017.07.31. Also made a comparison with conventional statistics, prediction tools; ARIMA. As a result, recurrent net, especially RNN outperformed and also could discover the applicability of LSTM to specific time-series (BDI).

• Ocean and Polar Research
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• v.34 no.2
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• pp.185-195
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• 2012

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

• Journal of Environmental Science International
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• v.26 no.2
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• pp.221-230
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• 2017

To determine the effect of air pollution reduction policies, the long-term trend of air pollutants should be analyzed. Kolmogorov-Zurbenko (KZ) filter is a low-pass filter, produced through repeated iterations of a moving average to separate each variable into its temporal components. The moving average for a KZ(m, p) filter is calculated by a filter with window length m and p iterations. The output of the first pass subsequently becomes the input for the next pass. Adjusting the window length and the number of iterations makes it possible to control the filtering of different scales of motion. To break down the daily mean $PM_{10}$ into individual time components, we assume that the original time series comprises of a long-term trend, seasonal variation, and a short-term component. The short-term component is attributable to weather and short-term fluctuations in precursor emissions, while the seasonal component is a result of changes in the solar angle. The long-term trend results from changes in overall emissions, pollutant transport, climate, policy and/or economics. The long-term trend of the daily mean $PM_{10}$ decreased sharply from $59.6ug/m^3$ in 2002 to $44.6ug/m^3$ in 2015. This suggests that there was a long-term downward trend since 2005. The difference between the unadjusted and meteorologically adjusted long-term $PM_{10}$ is small. Therefore, we can conclude that $PM_{10}$ is unaffected by the meteorological variables (total insolation, daily mean temperature, daily mean relative humidity, daily mean wind speed, and daily mean local atmospheric pressure) in Busan.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.6
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• pp.758-769
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• 2004

The trends and seasonalities of most time series have a large variability. The result of the Singular Spectrum Analysis(SSA) processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, forecasting by the SSA method should be applied to time series governed (may be approximately) by linear recurrent formulae(LRF). This study examined forecasting ability of SSA-LRF model. These methods are applied to daily water demand data. These models indicate that most cases have good ability of forecasting to some extent by considering statistical and visual assessment, in particular forecasting validity shows good results during 15 days.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.835-837
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• 1996

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network(Back-propagation) is proposed. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.60-67
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• 2003

For risk or reliability assessment of ship structures against particular hazardous situations such as total loss or sinking due to hull girder collapse, the short-term based response analysis rather than the long-term response analysis is required to determine wave-induced bending moments when the ship encounters a storm of specific duration and with a specified small encounter probability. In the present study, the effects of operational condition and sea states on wave-induced bending moments of large merchant vessels are investigated. A series of the short-term response analyses for a hypothetical VLCC and a Capesize bulk carrier (CSBC) are carried out with varying operational condition and sea states which include ship speed, significant wave height and wave persistence time, using the linear-strip theory based program ABS/SHIPMOTION and the MIT sea-keeping tables. The computed results are also compared with the IACS design formula predictions. The results and insights developed from the present study are summarized.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.239-250
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• 2022

This study examined the level of capital structure and its determinants of publicly traded companies in Malaysia before and after the COVID-19 pandemic. The data for this study was examined using Python Programming Language and time-series financial data from 2,784 quarterly observations in 2019 and 2020. The maximum debt is larger before the COVID-19 period, according to the findings. During the COVID-19 period, short-term debts and total debts have both decreased slightly. However, long-term debts have increased marginally. As a result, this research demonstrates that the capital structure has changed slightly during the COVID-19 period. The findings imply that independent of the capital structure proxies, tangibility, liquidity, and business size had an impact on capital structure in both periods. It was found that profitability had a significant impact on total debts both before and after the COVID-19 crisis. While higher-profit enterprises appear to have lesser short-term debts before the COVID-19 periods, they are also more likely to have lower long-term debts during the COVID-19 periods. Even though growing companies tend to have higher short-term debts and thus total debts during those periods, longterm debts are unaffected by potential growth.