• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• IE interfaces
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• v.18 no.3
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.201-207
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• 2023

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.111-120
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• 2011

High-rise buildings have recently increased over the residential, commercial and office facilities, thus an understanding of construction cost for high-rise building projects has been a fundamental issue due to enormous construction cost as well as unpredictable market conditions and fluctuations in the rate of inflation by long-term construction periods of high-rise projects. Especially, recent violent fluctuations of construction material prices add to problems in construction cost forecasting. This research, therefore, develops a time-series model with the Box-Jenkins methodologies and material prices time-series data in Korea in order to forecast future trends of unit prices of required materials. BIM (Building Information Modeling) approaches are also used to analyze injection time of construction resources and to conduct quantity takeoff so that total material price can be forecasted. Comparative analysis of Predictability of tentative ARIMA (Autoregressive Integrated Moving Average) models was conducted to determine optimal time-series model for forecasting future price trends. Proposed BIM based time series forecasting model can help to deal with sudden changes in economic conditions by estimating future material prices.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.370-373
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• 2007

지하구내에 물이 유입되기 시작하면 미리 설치되어 있는 펌프를 동작시켜서 배수를 해야한다. 이 경우 지하구내로 유입되는 물의 경로로 구분한 내수와 외수의 양을 알 수 있다면 위험도의 평가에 있어서 좋지만 유입량을 정확히 알 수 없으므로 시계열 분석으로 미래의 값을 예측하는 방법을 제시하고자 한다. 시계열 분석으로 예측한 값을 토대로 퍼지 이론을 이용한 지하구내 침수 상황 통제 시스템을 구현하였다.

• Journal of Agricultural Extension & Community Development
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• v.19 no.2
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• pp.301-320
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• 2012

The study uses a seasonal ARIMA model to forecast the number of tourists of Yeongdeok in an uni-variable time series. The monthly data for time series were collected ranging from 2006 to 2011 with some variation between on-season and off-season tourists in Yeongdeok county. A total of 72 observations were used for data analysis. The forecast multiplicative seasonal ARIMA(1,0,0)$(0,1,1)_{12}$ model was found the most appropriate one. Results showed that the number of tourists was 10,974 thousands in 2012 and 13,465 thousands in 2013, It was suggested that the grasping forecast model is very important in respect of how experts in tourism development in Yeongdeok county, policy makers or planners would establish strategies to allocate service in Yeongdeok tourist destination and provide tourism facilities efficiently.

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.402-408
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• 2010

Forecasting have qualitative and quantitative methods. Quantitative one analyze macro-economic factors such as the rate of exchange, oil price, interest rate and also predict the micro-economic factors such as sales and demands. Applying various statistical methods depends on the type of data. when data has seasonality and trend, Time Series analysis is proper but when it has casual relation, Regression analysis is good for this. Time Series and Regression can be used together. This study investigate artificial neural networks which is predictive technique for casual relation and try to compare the accuracy of forecasting between regression analysis and artificial neural network.