• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1133-1140
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• 2008

The goal of this paper is to analysis of the trend for stream quality in Gumi industrial complex with Winters' exponential smoothing method. It used the five different monthly time series data such as BOD, COD, TN, TP and EC from January 1998 to December 2006. The data of BOD, COD, TN, TP and EC are analyzed by time series method and forecasted the trends until December 2007. The stream qualities change for the better about BOD, COD, TN and TP, but the stream qualities resulted by EC is still serious.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.97.1-103
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• 2001

Railway passenger demand forecasts may be used directly, or as inputs to other optimization model which is use the demand forecasts to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

• Journal of the Korean Society for Railway
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• v.7 no.1
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• pp.9-13
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• 2004

Railway passenger demand forecasts may be used directly, or as inputs to other optimization models use them to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Nutritional Sciences
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• v.9 no.3
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• pp.201-211
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• 2006

The purpose of this study was to provide dietitians with the guidance in forecasting meal counts for a university/college foodservice facility. The forecasting methods to be analyzed were the following: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winters' methods, and simple linear regression. The accuracy of the forecasting methods was measured using mean squared error and Theil's U-statistic. This study showed how to project meal counts using 10 forecasting methods for dietitians. The results of this study showed that WES was the most accurate forecasting method, followed by $na\ddot{i}ve$ 2 and naive 3 models. However, naive model 2 and 3 were recommended for using by dietitians in university/college dining facilities because of the accuracy and ease of use. In addition, the 2000 spring semester data were better than the 2000 fall semester data to forecast 2001spring semester data.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.117-124
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• 2009

If time series data with seasonal effect exist, various statistical models like winters for successful forecasts could be used. But if the data are not enough to estimate seasonal effect, not much methods are available. This paper proposes the statistical forecasting method based on cumulative data when the data are not enough to estimate seasonal effect. We apply this method to real cosmetic sales data and show its better performance over moving average method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.419-430
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• 2014

An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.63-75
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• 2002

To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.