• 디지털융복합연구
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• 제18권12호
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• pp.87-95
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• 2020

인천공항은 대한민국으로 들어오거나 나가는 관문으로 나라의 이미지에 큰 영향을 미치므로 공항의 서비스 질을 유지하기 위해선 장기적인 공항 이용객 수 예측이 필요하다. 본 연구에서는 인천공항의 이용객 수요를 예측하기 위한 다양한 시계열 모형의 예측성능을 비교하였다. 인천공항 이용객 자료를 2002년 1월부터 2019년 12월까지 월 단위로 수집하여 살펴보면 일반적인 시계열자료에서 보이는 추세성과 계절성을 지니고 있다. 본 연구에서는 추세성과 계절성이 고려된 나이브 기법, 분해법, 지수 평활법, SARIMA, 그리고 PROPHET을 이용하여 단기, 중기, 장기예측 시계열모형을 비교하였다. 분석결과 단기예측은 최근 자료에 가중치를 준 지수 평활법이 우수했고 예상 2020년 연간 이용객 수는 약 7,350만명이다. 3년 후 인 2022년 중기예측은 정상성이 고려된 SARIMA모형이 우수하였고 예상 연간 이용객 수는 약 7,980만명이다. 4단계 인천공항 건설사업이 완료되는 2024년 예상 연간 여객수용 인원은 9,910만명이고 PROPHET모형이 가장 우수하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권5호
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• pp.233-240
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• 2001

This paper presents a time series prediction method using a fuzzy rule-based system. Extracting fuzzy rules by performing a simple one-pass operation on the training data is quite attractive because it is easy to understand, verify, and extend. The simplest method is probably to relate an estimate, x(n+k), with past data such as x(n), x(n-1), ..x(n-m), where k and m are prefixed positive integers. The relation is represented by fuzzy if-then rules, where the past data stand for premise part and the predicted value for consequence part. However, a serious problem of the method is that it cannot handle nonstationary data whose long-term mean is varying. To cope with this, a new training method is proposed, which utilizes the difference of consecutive data in a time series. In this paper, typical previous works relating time series prediction are briefly surveyed and a new method is proposed to overcome the difficulty of prediction nonstationary data. Finally, computer simulations are illustrated to show the improved results for various time series.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• 한국대기환경학회지
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• 제21권4호
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• pp.413-422
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• 2005

The ozone concentration measured by HALOE (Ver 19) from Oct. 1991 to Dec. 2003 is used for analyzing the variation of ozone concentration. The HALOE loaded in UARS is observing several gases in the atmosphere, from 10km to 80km. Fourier analysis of these data in the middle latitude northern hemisphere is reported in this paper. To detect any possible long term trends, the fourier transformed time series was back transformed after removing signals with time periods of less than 6 months. Although the results clearly show the strong annual cycle, it is difficult to show any long term trends from the fourier series. We also compared the ozone volume mixing ratio's from HALOE with that from the ground-based radiometry to evaluate the accuracy of microwave observation at Sookmyung Women's University.

• KDI Journal of Economic Policy
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• 제34권3호
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• pp.187-214
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• 2012

우리나라 국민소득 통계는 한국은행에 의해 1953년부터 공식적으로 발표되고 있지만 UN이 제시한 매뉴얼인 "1993 SNA"에 의해 작성된 1970년 이후의 현행 계열과 "1953 SNA"에 의해 작성된 1953~70년의 구계열로 시계열이 단절되어 있다. 더구나 2009년에 한국은행이 1970년 이후 현행 계열에 연쇄가중법을 도입하면서 고정가중법에 의한 기존의 시계열과 더 큰 차이를 보이게 되었다. 본고에서는 UN이 발표한 각종 국민계정 매뉴얼, 우리나라의 과거 산업연관표 등을 활용하여 1953년부터 1970년까지의 구계열에도 포괄범위를 일치시키고 연쇄가중치를 적용하여 1953년부터 2010년까지의 장기 시계열을 일관된 기준으로 구해 보았다. 수정 계열은 구계열에 비해 1953년 경상 기초가격 GDP가 3.5% 높아졌고 성장률은 1953~70년 중 평균 1.5%p 상승한 것으로 나타났다. 한편, 수정 계열을 이용하여 지난 60년간의 우리 경제 변화상을 살펴본 결과 경제규모가 50배 이상 커진 것으로 나타났다. 산업별로는 제조업 및 SOC 산업은 크게 확대된 반면 서비스업은 상대적으로 확대 폭이 작았다.

• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.521-536
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• 2021

As life expectancies increase continuously over the world, the accuracy of forecasting mortality is more and more important to maintain social systems in the aging era. Currently, the most popular model used is the Lee-Carter model but various studies have been conducted to improve this model with one of them being 6-parametric factor model (6-PFM) which is introduced in this paper. To this new model, long short-term memory (LSTM) and regularized LSTM are applied in addition to vector autoregression (VAR), which is a traditional time-series method. Forecasting accuracies of several models, including the LC model, 4-PFM, 5-PFM, and 3 6-PFM's, are compared by using the U.S. and Korea life-tables. The results show that 6-PFM forecasts better than the other models (LC model, 4-PFM, and 5-PFM). Among the three 6-PFMs studied, regularized LSTM performs better than the other two methods for most of the tests.

• 디지털융복합연구
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• 제10권9호
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• pp.357-362
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• 2012

본 논문에서는 분산이 각각 다른 이분산성을 갖는 비선형 시계열 자료를 가지고, 비선형 시계열 모형중 1차 일반화 확률계수 자기회귀모형(GRCA(1))과 자료의 형태에 상관없이 적용할 수 있는 신경망 모형을 이용하여 예측을 해서 어느 모형이 최소 평균예측오차제곱의 기준에서 비선형 시계열 자료의 예측에 적합한지를 비교 분석 하는 것이다. 조건부 이분산 모형에 따르는 자료로 확인된 종합주가지수 변동율에 대한 사례 분석 결과를 보면 신경망 모형은 단기 예측에서 좋은 예측 결과를 보였고, 비선형 모형인 GRCA(1) 모형은 장기 예측에서 좋은 예측 결과를 보여 주었다.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• 아동학회지
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• 제28권5호
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• pp.285-296
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• 2007

The purpose of this study was to estimate the future demand for certified teachers at childcare centers. This is an essential step to secure the supply of childcare teachers in the future. To achieve this purpose, the demand for childcare teachers from 2006 to 2020 were estimated using time series techniques with data on the number of childcare teachers from 2002 to 2005. According to time series estimates, the demand for childcare teachers is expected to increase steadily from 1,224 to 1,956 annually. This illustrates the need for mid-term and long-term planing in order to guarantee an adequate supply of childcare teachers.

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