A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm: An Application to the Data of Processed Cooked Rice

Takeyasu, Hiromasa,Higuchi, Yuki,Takeyasu, Kazuhiro Korean Institute of Industrial Engineers 2013 Industrial Engineeering & Management Systems Vol.12 No.3

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm

Hiromasa Takeyasu,Yuki Higuchi,Kazuhiro Takeyasu 대한산업공학회 2013 Industrial Engineeering & Management Systems Vol.12 No.3

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

Improving Forecasting Accuracy for the Daily Sanitary Materials’ Shipping Data With an Introduction of a Day of the Week Index

Kazuhiro Takeyasu,Hirotake Yamashita,Daisuke Takeyasu 대한산업공학회 2015 대한산업공학회 추계학술대회논문집 Vol.2015 No.11

In Supply Chain Management, how to improve forecasting accuracy such as sales, shipping is an important issue. There are many researches made on this. In this paper, a hybrid method is introduced and plural methods are compared. “a day of the week index” is newly introduced to the daily shipping data of sanitary materials and we have obtained good result.

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm

Daisuke Takeyasu,Kazuhiro Takeyasu 대한산업공학회 2015 대한산업공학회 추계학술대회논문집 Vol.2015 No.11

In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. There are many researches made on this. In this paper, a hybrid method is introduced and plural methods are compared. Trend removing by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is executed to the data of Wheelchairs for three cases (Sum total data of Wheelchairs, Manually propelled wheelchairs and Electric wheelchairs). Genetic Algorithm is utilized to search the optimal weight for the weighting parameters of linear and non-linear function. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

Intermittent Demand Forecasting in the Case of Medical Apparatus By Improving Forecasting Accuracy

Daisuke Takeyasu,Asami Shitara,Kazuhiro Takeyasu 대한산업공학회 2015 대한산업공학회 추계학술대회논문집 Vol.2015 No.11

Intermittent data are often seen in industries. But it is rather difficult to make forecasting in general. In recent years, the needs for intermittent demand forecasting are increasing because of the constraints of strict Supply Chain Management. How to improve the forecasting accuracy is an important issue. There are many researches made on this. But there are rooms for improvement. In this paper, a new method for cumulative forecasting method is proposed. The data is cumulated and to this cumulated time series, the following method is applied to improve the forecasting accuracy. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The forecasting result is compared with those of the non-cumulative forecasting method. The new method shows that it is useful for the forecasting of intermittent demand data.

On a Multiple Data Handling Method under Online Parameter Estimation

Takeyasu, Kazuhiro,Amemiya, Takashi,Iino, Katsuhiro,Masuda, Shiro Korean Institute of Industrial Engineers 2002 Industrial Engineeering & Management Systems Vol.1 No.1

In the field of plant maintenance, data that are gathered by sensors on multiple machines are handled and analyzed. Online or pseudo online data handling is required on such fields. When the data occurrence speed exceeds the data handling speed, multiple data should be handled at a time (batch data handling or pseudo online data handling). If l amount of data are received at one time following N amount of data, how to estimate the new parameters effectively is a great concern. A new simplified calculation method, which calculates the N data's weights, is introduced. Numerical examples show that this new method has a fairly god estimation accuracy and the calculation time is less than 1/10 compared with the case when the whole data are re-calculated. Even under the restriction calculation ability in the apparatus is limited, this proposed method makes the failure detection of equipments possible in early stages with a few new coming data. This method would be applicable in many data handling fields.

Analysis of The Behavior of Kurtosis By Simplified Model of One Sided Affiliated Impact Vibration

Takeyasu, Kazuhiro,Higuchi, Yuki Korean Institute of Industrial Engineers 2005 Industrial Engineeering & Management Systems Vol.4 No.2

Among many amplitude parameters, Kurtosis (4-th normalized moment of probability density function) is recognized to be the sensitive good parameter for machine diagnosis. Kurtosis has a value of 3.0 under normal condition and the value generally goes up as the deterioration proceeds. In this paper, simplified calculation method of kurtosis is introduced for the analysis of impact vibration with one sided affiliated impact vibration which occurs towards the progress of time. That phenomenon is often watched in the failure of such as bearings’ outer race. One sided affiliated impact vibration is approximated by one sided triangle towards the progress of time and simplified calculation method is introduced. Varying the shape of one sided triangle, various models are examined and it is proved that new index is a sensitive good index for machine failure diagnosis. Utilizing this method, the behavior of kurtosis is forecasted and analyzed while watching machine condition and correct diagnosis is executed.

Simplified Machine Diagnosis Techniques by Impact Vibration using n-th Moment of Absolute Deterioration Factor

Takeyasu, Kazuhiro,Amemiya, Takashi,Tanaka, Jumpei,Masuda, Shiro Korean Institute of Industrial Engineers 2005 Industrial Engineeering & Management Systems Vol.4 No.1

Among many dimensional and dimensionless amplitude parameters, kurtosis (4-th normalized moment of probability density function) is generally regarded as a sensitive good parameter for machine diagnosis. However, higher order moment may be supposed to be much more sensitive. Bicoherence is an absolute deterioration factor whose range is 1 to 0. The theoretical value of n-th moment divided by n-th moment calculated by measured data would behave in the same way. We propose a simplified calculation method for an absolute index of n-th moment and name this as simplified absolute index of n-th moment. Some favorable results are obtained.

Machine Diagnosis Techniques by Simplified Calculation Method

Takeyasu, Kazuhiro,Amemiya, Takashi,Iino, Katsuhiro,Masuda, Shiro Korean Institute of Industrial Engineers 2003 Industrial Engineeering & Management Systems Vol.2 No.1

Among many dimensional or dimensionless amplitude parameters, kurtosis and ID Factor are said to be sensitive good parameters for machine diagnosis. In this paper, a simplified calculation method for both parameters is introduced when impact vibration arise in the observed data. Compared with the past papers' results, this new method shows a good result which fit well. This calculation method is simple enough to be executed even on a pocketsize calculator and is very practical at the factory of maintenance field. This can be installed in microcomputer chips and utilized as a tool for early stage detection of the failure.

Development of QZSS L1-SAIF Augmentation Signal

Takeyasu Sakai,Sonosuke Fukushima,Ken Ito 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

QZSS (quasi-zenith satellite system) is a Japanese satellite navigation program offering GPS augmentationsignal, called L1-SAIF (submeter-class augmentation with integrity function), on the GPS L1 frequency. L1-SAIF augmentation will improve user position accuracy within 1 meter RMS nationwide based on wide-area differential GPS technique and provide users with the integrity information necessary for safety of mobile users. L1-SAIF augmentation messages are generated at L1-SAIF Master Station (L1SMS) and broadcast to the users. The ENRI has been developing L1SMS as a part of the national QZSS program. In this paper the authors describe the overview of L1-SAIF signal, the configuration of L1SMS, and some initial results of realtime operation test.