• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.38.1-38.1
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• 2017

Symbiotic stars are long-orbital-period interacting binaries characterized by extended emission over the whole electromagnetic range and by complex photometric and spectroscopic variability. In this contribution, I will present some high-cadence, long-term optical light curves of confirmed and candidate symbiotic stars in the Magellanic Clouds. By careful visual inspection and combined time series analysis techniques, we investigate for the first time in a systematic way the photometric properties of these astrophysical objects, trying in particular to distinguish the evolutionary status of the cool component, to provide its first-order pulsation ephemeris and to link all this information with the physical parameters of the binary system as a whole. Finally, I will discuss a new, promising photometric technique, potentially able to discover Symbiotic Stars in the Local Group of Galaxies without the recourse to costly spectroscopic follow-up.

• Magazine of the Korean Society of Agricultural Engineers
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• v.23 no.4
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• pp.46-52
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• 1981

The aim at this study has the stochastic hydrological analysis for the annual mean discharge and monthly discharge which were observed at Lobith of River Rhine in the Netherlands from 1901 to 1972. After this study was analysed by computer IBM 370 and Hewlett Parkard 9800, the results were as follows; 1.When 72 data was divided into two groups of subsample data as 36 data, they do not have their properties to be non-homogeneous and inconsistent due to F-test and t-test. 2.The credit limits of the serial correlation coefficient was fluctuated $\pm$0. 231 which was shown in Fig. 3. at significant level 99% by Anderson's test. 3.The correlogram at short term was shown to be no short-term persistence as Fig. 3. 4.Since the correlogram at long term has displayed that Hurst's coefficient was 0.6144 between 0.6 and 0.7, it was to be no long-term persistence. 5.The stochastic model with annual discharge of this River Rhine was shown with $\chi$t=2195+483. 8 $\varepsilon$t as $\chi$t=$\mu$+oet and $\varepsilon$t=$_1$ø$\varepsilon$t-$_1$+ζt where t=1,2,3,..., ζt is an independent series with mean zero and variance (1-ø2), $\varepsilon$t is the dependent series, and 4' is the parameter of the model. 6.The serial correlation coefficient of monthly discharge was explained as $\chi$$_1$ = 0.34 . sin(6-$\pi$t+$\pi$) as Fig.4. and the River Rhine has no large fluctuation and smoothly changed during that time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.271-282
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• 2021

In recent years, a significant number of empirical studies have examined the relationship between export and economic growth in India. However, this study analyses the relationship between exports and economic growth through the time series model. The main aim of this study is to investigate the causal relationship between exports and economic growth in India. The VAR model was used for the period 1961 to 2015 after verifying the stationarity of the variables through using Augmented Dickey-Fuller and Phillip-Perron tests. The Indian export sector has been found to have a significant and positive impact on economic growth and other long-term economic activities. The study also employed the Granger causality test to check the direction of causality and found that RXGS, RGDP, RPFC, and RGFC had a unidirectional relationship and RXGS and RMGS had a bidirectional relationship in long run. Also, the findings of this study suggest that a steady-state between exports and economic growth can be achieved in India over a long period. The overall outcome of this study provides a testimony of the fact that the export sector plays a vital role in economic growth in India and also leads to the long-term growth of other economic activities.

• Smart Media Journal
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• v.9 no.4
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• pp.26-35
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• 2020

Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.101-109
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• 2018

In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.329-338
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• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.91-91
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• 2011

In the present study, the proposed EMD and NSOR models has been applied in hydrology and climate sciences. Here, we present those applications as the following: (1) to extend future scenarios of Global Surface Temperature Anomaly including long-term oscillation component; (2) to extend the future evolution of the Eastern Canada winter precipitation; (3) to apply EMD in detecting climate change.