• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.1-13
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• 2021

In this study, a time series analysis technique was applied to calibrate and predict traffic data for various purposes, such as planning, design, maintenance, and research. Existing algorithms have limitations in application to data such as traffic data because they show strong periodicity and seasonality or irregular data. To overcome and supplement these limitations, we applied the SARIMA model, an analytical technique that combines the autocorrelation model, the Seasonal Auto Regressive(SAR), and the seasonal Moving Average(SMA). According to the analysis, traffic volume prediction using the SARIMA(4,1,3)(4,0,3) 12 model, which is the optimal parameter combination, showed excellent performance of 85% on average. In addition to traffic data, this study is considered to be of great value in that it can contribute significantly to traffic correction and forecast improvement in the event of missing traffic data, and is also applicable to a variety of time series data recently collected.

• Journal of environmental and Sanitary engineering
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• v.15 no.2
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• pp.65-74
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• 2000

The purpose of this study is to develop the stochastic stream water quality model for the intake station of Chung-Ju city waterworks in the Han river system. This model was based on the theory of Box-Jenkins Multiplicative ARIMA(SARIMA) and the state space model to simulate changes of water qualities. Variable of water qualities included in the model are temperature and dissolved oxygen(DO). The models development were based on the data obtained from Jan. 1990 to Dec. 1997 and followed the typical procedures of the Box-Jenkins method including identification and estimation. The seasonality of DO and temperature data to formulate for the SARIMA model are conspicuous and the period of revolution was twelve months. Both models had seasonality of twelve months and were formulates as SARIMA {TEX}$(2,1,1)(1,1,1)_{12}${/TEX} for DO and temperature. The models were validated by testing normality and independency of the residuals. The prediction ability of SARIMA model and state space model were tested using the data collected from Jan. 1998 to Oct. 1999. There were good agreements between the model predictions and the field measurements. The performance of the SARIMA model and state space model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the state space model lead to the improved accuracy.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1075-1081
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• 2016

The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.841-846
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• 2002

We applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the line utilization that QoS of the network is greatly influenced by. And this paper proposes the learning algorithm of dynamic threshold in line utilization using the SARIMA model. We can find the proper dynamic threshold in timely line utilization on the various network environments and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold on real network. Network manager can overcome a shortcoming of original threshold method and maximize the performance of this algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.18-26
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• 2015

This study is a fundamental research to suggest a forecasting model for short-term railway passenger demand focusing on major lines (Gyeungbu, Honam, Jeonla, Janghang, Jungang) of Saemaeul rail and Mugunghwa rail. Also the author tried to verify the potential application of the proposed models. For this study, SARIMA model considering characteristics of seasonal trip is basically used, and daily mean forecasting models are independently constructed depending on weekday/weekend in order to consider characteristics of weekday/weekend trip and a legal holiday trip. Furthermore, intervention events having an impact on using the train such as introduction of new lines or EXPO are reflected in the model to increase reliability of the model. Finally, proposed models are confirmed to have high accuracy and reliability by verifying predictability of models. The proposed models of this research will be expected to utilize for establishing a plan for short-term operation of lines.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.191-209
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• 2022

This paper is a review of studies that focus on the prediction of a won/dollar exchange rate before and after the covid 19 pandemic. The Korea economy has an unprecedent situation starting from 2021 up till 2022 where the won/dollar exchange rate has exceeded 1,400 KRW, a first time since the global financial crisis in 2008. The US Federal Reserve has raised the interest rate up to 2.5% (2022.7) called a 'Big Step' and the Korea central bank has also raised the interested rate up to 2.5% (2022.8) accordingly. In the unpredictable economic situation, the prediction of the won/dollar exchange rate has become more important than ever. The authors separated the period from 2015.Jan to 2022.Aug into three periods and built a best fitted ARIMA/ARDL prediction model using the period 1. Finally using the best the fitted prediction model, we predicted the won/dollar exchange rate for each period. The conclusions of the study were that during Period 3, when the usual relationship between exchange rates and economic factors appears, the ARDL model reflecting the variable relationship is a better predictive model, and in Period 2 of the transitional period, which deviates from the typical pattern of exchange rate and economic factors, the SARIMA model, which reflects only historical exchange rate trends, was validated as a model with a better predictive performance.

• Korea Trade Review
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• v.47 no.6
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• pp.291-305
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• 2022

In this study, the volume of Pohang Port was predicted. All cargo of Pohang port, iron ore, steel, and bituminous coals were selected as prediction targets. SARIMA, Prophet, and Neural Prophet were used as analysis methods. The predictive power of each model was verified, and a predictive model with high performance was used to predict the volume of goods in Pohang port. As a result of the analysis, it was found that Neural Prophet showed the highest performance in all predictive power. As a result of predicting the future volume of goods until August 2027 using Neural Prophet, it was found that the volume of all items in Pohang port was decreasing. In particular, it was analyzed that the decline in steel cargo was steep. In order to increase the volume of cargo at Pohang port, it is necessary to diversify the cargo handled at Pohang port and check the policy of increasing the volume of cargo.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.