• 무역학회지
• /
• 제46권6호
• /
• pp.191-209
• /
• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.338-341
• /
• 2003

Recently, the chaotic method is employed to forecast a near future of uncertain phenomena. This method makes it possible by restructuring an attractor of given time-series data in multi-dimensional space through Takens' embedding theory. However, many economical time-series data are not sufficiently chaotic. In other words, it is hard to forecast the future trend of such economical data on the basis of chaotic theory. In this paper, time-series data are divided into wave components using wavelet transform. It is shown that some divided components of time-series data show much more chaotic in the sense of correlation dimension than the original time-series data. The highly chaotic nature of the divided component enables us to precisely forecast the value or the movement of the time-series data in near future. The up and down movement of TOPICS value is shown so highly predicted by this method as 70％.

• 응용통계연구
• /
• 제35권5호
• /
• pp.645-655
• /
• 2022

본 논문에서는 R에서 시계열 자료 예측을 위한 자동화 함수에 대하여 고찰하고 그 예측 성능을 비교합니다. 대표적인 시계열 예측 방법인 지수 평활 모형과 ARIMA (autoregressive integrated moving average) 모형을 대상으로 하였으며, 이들의 모형화 및 예측 자동화를 가능하게 하는 R의 4가지 자동화 함수인 forecast::ets(), forecast::auto.arima(), smooth::es()와 smooth::auto.ssarima()를 대상으로 하였습니다. 이들의 예측 성능을 비교하기 위하여 3,003가지의 시계열로 구성되어 있는 M3-Competition자료와 3가지의 정확성 척도를 사용하였습니다. 4가지 자동화 함수는 모형화의 다양성 및 편리성, 예측 정확도 및 실행 시간 등에서 각자 장단점이 있음을 확인하였습니다.

• Journal of information and communication convergence engineering
• /
• 제9권4호
• /
• pp.369-374
• /
• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• 한국정보통신학회논문지
• /
• 제13권8호
• /
• pp.1647-1652
• /
• 2009

비선형 동력학 시스템으로 구성된 전력 수요의 시계열 데이터를 예측하기 위해 적용된 신경망 및 퍼지 적응 알고리즘 등은 예측오차가 상대적으로 크게 나타났다. 이는 전력수요 시계열 데이터가 가지고 있는 카오스적인 성질에 기인하며 이중 초기값에 민감한 의존성은 장기적인 예측을 더욱더 어렵게 하는 요인으로 작용한다. 전력수요 시계열 데이터가 가지고 있는 카오스적인 성질을 정량 및 정성적인 방식으로 분석 을 수행하고, 시스템 동력학적 특성의 정량분석에 이용되는 Lyapunov 지수를 이용하여 어트랙터 재구성, 다차원 카오스 시계열 데이터를 예측하는 방식으로 수요예측 시뮬레이션을 수행하고 결과를 비교 평가하여 기존 제안방식보다 실용적이며 효과적임을 확인한다.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2009년도 춘계학술대회
• /
• pp.171-174
• /
• 2009

비선형 동력학 시스템으로 구성된 전력 수요의 시계열 데이터를 예측하기 위해 적용된 신경망 및 퍼지 적응 알고리즘 등은 예측오차가 상대적으로 크게 나타났다. 이는 전력수요 시계열 데이터가 가지고 있는 카오스적인 성질에 기인하며 이중 초기값에 민감한 의존성은 장기적인 예측을 더욱더 어렵게 하는 요인으로 작용한다. 전력수요 시계열 데이터가 가지고 있는 카오스적인 성질을 정량 및 정성적인 방식으로 분석을 수행하고, 시스템 동력학적 특성의 정량분석에 이용되는 Lyapunov 지수를 이용하여 어트랙터 재구성, 다차원 카오스 시계열 데이터를 예측하는 방식으로 수요예측 시뮬레이션을 수행하고 결과를 비교 평가하여 기존 제안방식보다 실용적이며 효과적임을 확인한다.

• 한국경영과학회지
• /
• 제24권1호
• /
• pp.79-98
• /
• 1999

There have been a number of empirical studios on the effectiveness of Judgmental adjustment to statistical forecasts Generally the results have been mixed. This study examined the impact of the reliability and the source of the additionally presented reference forecast upon the revision process in a longitudinal time series forecasting task with forecast support systems. A 2-between(reliability ＆ source). 2-within(seasonality ＆ block) factorial experiment was conducted with post-graduate students using real time series. Judgmental adjustment was found to improve the accuracy of initial eyeballing irrespective of the reliability of an additionally presented forecast. But it did not outperform the dampened reference forecast. No effect was found of the way the source of the reference forecast was framed. Overall the subjects anchored heavily on their Initial forecast and relied too little on the reference forecast irrespective of its reliability. Moreover they did not improve at the task over time, despite immediate outcome feedback.

• 응용통계연구
• /
• 제9권1호
• /
• pp.125-137
• /
• 1996

시계열 자료는 흔히 반복되지 않는 비정상적인 사건의 영향으로 이상치를 포함한다. 시계열 자료는 관측치들 사이에 종속구조를 갖기 때문에, 이상치의 영향은 다른 통계적 분석에서 보다 더 심각할 수 있다. 본 논문에서는 연속이상치가 예측에 미치는 영향을 파악하는 데에 촛점을 두었다. 특히, l 시점 후 예측오차의 평균제곱의 증가량을 유도하고, 이 증가량으로 연속이상치가 예측에 미치는 영향을 측정하였다. 일반적으로, 연속이상치가 예측 원점에서 아주 가까운 시점에서 발생하지 않았으며 그 증가량은 크지 않음을 밝히고, 실제 자료를 분석하여 확인하였다.

• The Journal of Asian Finance, Economics and Business
• /
• 제9권4호
• /
• pp.83-92
• /
• 2022

Due to the uncertainty in the order of the integrated model, the SARIMA-LSTM model, SARIMA-SVR model, LSTM-SARIMA model, and SVR-SARIMA model are constructed respectively to determine the best-combined model for forecasting the China-Russia trade turnover. Meanwhile, the effect of the order of the combined models on the prediction results is analyzed. Using indicators such as MAPE and RMSE, we compare and evaluate the predictive effects of different models. The results show that the SARIMA-LSTM model combines the SARIMA model's short-term forecasting advantage with the LSTM model's long-term forecasting advantage, which has the highest forecast accuracy of all models and can accurately predict the trend of China-Russia trade turnover in the post-epidemic period. Furthermore, the SARIMA - LSTM model has a higher forecast accuracy than the LSTM-ARIMA model. Nevertheless, the SARIMA-SVR model's forecast accuracy is lower than the SVR-SARIMA model's. As a result, the combined models' order has no bearing on the predicting outcomes for the China-Russia trade turnover time series.

• Communications for Statistical Applications and Methods
• /
• 제19권6호
• /
• pp.869-876
• /
• 2012

In this paper, we applied seasonal time series models such as ARIMA, FARIMA, AR-GARCH and Holt-Winters in consideration of seasonality to forecast short-term electricity demand data. The results for performance evaluation on the time series models show that seasonal FARIMA and seasonal Holt-Winters models perform adequately under the criterion of Mean Absolute Percentage Error(MAPE).