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Time series interval forecast using GM(1,1) and NGBM(1, 1) models.

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Title: Time series interval forecast using GM(1,1) and NGBM(1, 1) models.
Authors: Chen, Ying-Yuan1, Liu, Hao-Tien2 htliu@isu.edu.tw, Hsieh, Hsiow-Ling3
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Mar2019, Vol. 23 Issue 5, p1541-1555. 15p.
Subjects: Time series analysis, Gray forecasting model, Prediction models, Mathematical bounds, Nonnegative matrices
Abstract: Grey forecast is used for few and uncertain data, and its forecast results have very high accuracy. Although numerous researchers have developed various grey forecasting models, the forecast results of these models are limited to single-point forecast values and cannot provide more valuable information (e.g. possible estimation range) for decision-makers. In order to address this problem, this paper proposes two grey interval forecasting methods: interval GM(1, 1) and interval NGBM(1, 1), for few and uncertain time series data. To evaluate the forecast accuracy of the two grey interval methods, this study took the short-term forecast of the passenger volume of Taiwan High Speed Rail as an example and compared the forecast accuracy of the proposed two methods with that of three current grey forecasting methods. The forecast results showed that the proposed two methods have the highest forecast accuracy among the five grey forecasting methods. The grey interval forecast value provided by the proposed methods can help decision-makers make more accurate judgement within a probable variation range. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Gray+forecasting+model%22">Gray forecasting model</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+bounds%22">Mathematical bounds</searchLink><br /><searchLink fieldCode="DE" term="%22Nonnegative+matrices%22">Nonnegative matrices</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Grey forecast is used for few and uncertain data, and its forecast results have very high accuracy. Although numerous researchers have developed various grey forecasting models, the forecast results of these models are limited to single-point forecast values and cannot provide more valuable information (e.g. possible estimation range) for decision-makers. In order to address this problem, this paper proposes two grey interval forecasting methods: interval GM(1, 1) and interval NGBM(1, 1), for few and uncertain time series data. To evaluate the forecast accuracy of the two grey interval methods, this study took the short-term forecast of the passenger volume of Taiwan High Speed Rail as an example and compared the forecast accuracy of the proposed two methods with that of three current grey forecasting methods. The forecast results showed that the proposed two methods have the highest forecast accuracy among the five grey forecasting methods. The grey interval forecast value provided by the proposed methods can help decision-makers make more accurate judgement within a probable variation range. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00500-017-2876-0
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        Text: English
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        Type: general
      – SubjectFull: Gray forecasting model
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      – SubjectFull: Prediction models
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      – SubjectFull: Mathematical bounds
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      – SubjectFull: Nonnegative matrices
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      – TitleFull: Time series interval forecast using GM(1,1) and NGBM(1, 1) models.
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              Text: Mar2019
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