• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.89-98
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• 2007

In this paper the economic value of weather forecasts is valuated for profit-oriented enterprise decision-making situations. Value is estimated in terms of monetary profits (or benefits) resulted from the forecast user's decision under the specific payoff structure, which is represented by a profit/loss ratio model combined with a decision function and a value score (VS). The forecast user determines a business-related decision based on the probabilistic forecast, the user's subjective reliability of the forecasts, and the payoff structure specific to the user's business environment. The VS curve for a meteorological forecast is specified by a function of the various profit/loss ratios, providing the scaled economic value relative to the value of a perfect forecast. The proposed valuation method based on the profit/loss ratio model and the VS is adapted for hypothetical sets of forecasts and verified for site-specific probability of precipitation forecast of 12 hour and 24 hour-lead time, which is generated from Korea meteorological administration (KMA). The application results show that forecast information with shorter lead time can provide the decision-makers with great benefits and there are ranges of profit/loss ratios in which high subjective reliability of the given forecast is preferred.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.55-59
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• 2022

This paper introduce Renewable Energy forecasting technology, which is a part of renewable management system. Then, calculation method of dedicated transmission line's meteorological data to forecast renewable energy is suggested. As the case of dedicated transmission line, there is only power output data combined the number of renewable plants' output that acquired from circuit breakers. So it is need to calculate meteorological data for dedicated transmission line that matched combined power output data. this paper suggests two calculation method. First method is select the plant has the largest capacity, and use it's meteorological data as line meteorological data. Second method is average with weight that given according to plants' capacity. In case study, suggested methods are applied to real data. Then use calculated data to Renewable forecasting and analyze the forecasting results.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.297-309
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• 2024

This paper evaluates the performance of the neural network autoregressive model combined with an exponential smoothing model, called the NNARX+ETS model. The combined model utilizes the components of ETS as exogenous variables for NNARX, to forecast time series data using artificial neural networks. The main idea is to enhance the performance of NNAR using only lags of the original time series data, by combining traditional time series analysis methods with the neural networks through NNARX. We employ two real data for performance evaluation and compare the NNARX+ETS with NNAR and traditional time series analysis methods such as ETS and ARIMA (autoregressive integrated moving average) models.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.55-62
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• 2009

Collaborative filtering is a popular technique that recommends items based on the opinions of other people in recommender systems. Memory-based collaborative filtering which uses user database can be divided in user-based approaches and item-based approaches. User-based collaborative filtering predicts a user's preference of an item using the preferences of similar neighborhood, while item-based collaborative filtering predicts the preference of an item based on the similarity of items. This paper proposes a combined forecast scheme that predicts the preference of a user to an item by combining user-based prediction and item-based prediction using the ratio of the number of similar users and the number of similar items. Experimental results using MovieLens data set and the BookCrossing data set show that the proposed scheme improves the accuracy of prediction for movies and books compared with the user-based scheme and item-based scheme.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.135-141
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• 2015

The objective of this study is to develop an early warning system for rainfall induced landslides. For this study, we suggested an analysis process using rainfall forecast data. 1) For a selected slope, safety factor with saturated depth was analyzed and safety factor threshold was established (warning FS threshold=1.3, alarm FS threshold=1.1). 2) If rainfall started, saturated depth and safety factor was calculated with rainfall forecast data, 3) And every hour after safety factor is compared with threshold, then warning or alarm can issued. In the future, we plan to make a early warning system combined with the in-situ inclinometer sensors.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.1-13
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• 2022

This paper empirically explores the predicting ability of the newly proposed smooth transition (ST) time-varying combining forecast methods. The proposed method allows the "weight" of combining forecasts to change gradually over time through its unique feature of transition variables. Stock market returns from 7 countries were applied to Ad Hoc models, the well-known Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, and the Smooth Transition Exponential Smoothing (STES) models. Of the individual models, GJRGARCH and STES-E&AE emerged as the best models and thereby were chosen for constructing the combined forecast models where a total of nine ST combining methods were developed. The robustness of the ST combining forecasts is also validated by the Diebold-Mariano (DM) test. The post-sample forecasting performance shows that ST combining forecast methods outperformed all the individual models and fixed weight combining models. This study contributes in two ways: 1) the ST combining methods statistically outperformed all the individual forecast methods and the existing traditional combining methods using simple averaging and Bates & Granger method. 2) trading volume as a transition variable in ST methods was superior to other individual models as well as the ST models with single sign or size of past shocks as transition variables.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.