• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.339-347
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• 2021

This study aims to forecast the exchange rate by a combination of different models as proposed by Poon and Granger (2003). For this purpose, we include three univariate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Pakistani Rupee against the US dollar by a combination of different forecasting techniques. The observations from M1 2020 to M12 2020 are held back for in-sample forecasting. The models are then assessed through equal weightage and var-cor methods. Our results suggest that NARDL outperforms all individual time series models in terms of forecasting the exchange rate. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models with the lowest MAPE value of 0.612 suggesting that the Pakistani Rupee exchange rate against the US Dollar is dependent upon the macro-economic fundamentals and recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting, as stated by Poon and Granger (2003).

• 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.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.219-227
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• 2007

The time series models have been used to analyze and predict the network traffic. In this paper, we compare the performance of the time series models for prediction of network traffic. The feasibility study showed that a class of nonlinear time series models can be outperformed than the linear time series models to predict the network traffic.

• Journal of Korean Society of Rural Planning
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• v.25 no.4
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• pp.109-117
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• 2019

The purpose of this study is to present a time series model of onion and garlic prices. After considering the various time series models, we calculated the appropriate time series models for each item and then selected the model with the minimized error rate by reflecting the monthly dummy variables and import data. Also, we examined whether the predictive power improves when we combine the predictions of the Korea Rural Economic Institute with the predictions of time series models. As a result, onion prices were identified as ARMGARCH and garlic prices as ARXM. Monthly dummy variables were statistically significant for onion in May and garlic in June. Garlic imports were statistically significant as a result of adding imports as exogenous variables. This study is expected to help improve the forecasting model by suggesting a method to minimize the price forecasting error rate in the case of the unstable supply and demand of onion and garlic.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.807-813
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• 2012

In this paper, we introduce data mining techniques for short-term load forecasting (STLF). First, we use the K-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the KEPCO hourly record for 2008 and conclude that our approach is effective.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.511-516
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• 1996

In this paper, weekly maximum power demand forecasting method in consideration of temperature estimation using a time series model was presented. The method removing weekly, seasonal variations on the load and irregularities variation due to unknown factor was presented. The forecasting model that represent the relations between load and temperature which get a numeral expected temperature based on the past 30 years(1961~1990) temperature was constructed. Effect of holiday was removed by using a weekday change ratio, and irregularities variation was removed by using an autoregressive model. The results of load forecasting show the ability of the method in forecasting with good accuracy without suffering from the effect of seasons and holidays. Percentage error load forecasting of all seasons except summer was obtained below 2 percentage. (author). refs., figs., tabs.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.236-243
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• 2019

Hydrologic time series has been analyzed and forecasted by using classical linear models. 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. Daily streamflow series at St. Johns river near Cocoa, Florida, USA showed an interesting result of a low dimensional, nonlinear dynamical system but daily inflow at Soyang reservoir, South Korea showed stochastic property. Based on the chaotic dynamical characteristic, DVS (deterministic versus stochastic) algorithm is used for short-term forecasting, as well as for exploring the properties of the system. In addition to the use of DVS algorithm, a neural network scheme for the forecasting of the daily streamflow series can be used and the two techniques are compared in this study. As a result, the daily streamflow which has chaotic property showed much more accurate result in short term forecasting than stochastic data.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.139-153
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• 2018

This paper investigates solar power forecasting based on several time series models. First, we consider weather variables that influence forecasting procedures as well as compare forecasting accuracies between time series models such as ARIMAX, Holt-Winters and Artificial Neural Network (ANN) models. The results show that ten models forecasting 24hour data have better performance than single models for 24 hours.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.