• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.147-160
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• 2020

In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.981-993
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• 2015

The analysis of hydrologic time series data is crucial for the effective management of water resources. Therefore, it has been widely used for the long-term forecasting of hydrologic variables. In tradition, time series analysis has been used to predict a time series without considering exogenous variables. However, many studies using decomposition have been widely carried out with the assumption that one data series could be mixed with several frequent factors. In this study, the empirical mode decomposition method was performed for decomposing a hydrologic time series data into several components, and each component was applied to the time series models, autoregressive moving average (ARMA). After constructing the time series models, the forecasting values are added to compare the results with traditional time series model. Finally, the forecasted estimates from ARMA model with empirical mode decomposition method showed better performance than sole traditional ARMA model indicated from comparing the root mean square errors of the two methods.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.13-21
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• 2011

This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.263-264
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• 2012

본 논문은 여러 개의 독립적인 시계열로 구성된 시계열 패널 자료를 이용하여 비선형 모형인 GRCA모형과 신경망을 이용하여 예측값을 구하여 서로 비교 분석하고자 한다. 먼저 GRCA모형에 대하여 연구하고 신경망의 구조와 예측값을 구하기 위한 여러 가지 변환함수를 유도한다. 단기 예측에서는 신경망 방법의 예측값이 더 좋았고, 장기예측에서는 비선형모형을 이용한 예측값이 더 좋은 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.33-41
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• 2006

In this paper, we analyse the volatilities in financial data such as stock prices and exchange rates in term of a class of nonlinear time series models. We compare the performance of Generalized Autoregressive Conditional Heteroscadastic(GARCH) , Integrated GARCH(IGARCH), Exponential GARCH(EGARCH) models by KOSPI (Korean stock Prices Index) data. The estimation for the parameters in the models was carried out by the ML methods.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.631-638
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• 2002

Stochastic model has been widely used for the forecasting of time series. However, this study tries to perform the precipitation forecasting by fuzzy time series model using fuzzy concept. The published fuzzy based models are used for the forecasting of time series and also we suggest that the combination of fuzzy time series models and neuro-fuzzy system can increase the forecastibility of the models. The precipitation time series in illinois, USA is analyzed for the forecasting by the known fuzzy time series models and the suggested methodology in this study. As a result, we know that the suggested methodology shows more exact results than the known models.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.885-893
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• 2015

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

• Annual Conference of KIPS
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• 2021.05a
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• pp.509-512
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• 2021

최근 다양한 시계열 데이터의 분석이 딥러닝 방법을 통하여 수행되고 있다. 주로 RNN과 LSTM을 이용하여 많은 시계열 예측이 이루어지고 있다. 하지만 이러한 예측모델을 생성하는데 가장 중요한 것은 어떠한 변수를 얼마나 사용하는지가 중요하다. 이에 대하여, 본 연구에서는 3개의 신경망을 적용하여, 속성을 선택하는 Selection MLP, 속성에 가중치를 부여하는 Extraction MLP 그리고 예측을 진행하는 Prediction MLP로 이루어진 MLP-SEL 구조를 제안한다. 비교를 위하여 다른 순환 신경망에 대하여 시계열 데이터에 대한 예측을 진행하였으며, 그 결과 우리가 제안한 MLP-SEL 모델의 시계열 예측이 좋은 성능을 보였다.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.507-516
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• 2012

We study time series models for seasonal time series data with a covariance structure that depends on time and the periodic autocorrelation at various lags $k$. In this paper, we introduce an ARMA model with periodically varying coefficients(PARMA) and analyze Arosa ozone data with a periodic correlation in the practical case study. Finally, we use a PARMA model and a seasonal ARIMA model for data analysis and show the performance of a PARMA model with a comparison to the SARIMA model.

• The Journal of Bigdata
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• v.7 no.2
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• pp.217-224
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• 2022

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Therefore, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'A'. According to the simulation results, it was confirmed that Bidirectional-LSTM(Bi-LSTM) compared to LSTM(Long Short-Term Memory) takes more simulation time about more than 50%, but the prediction accuracy of non-periodic time series data such as clothing product sales data is the same.