Aggregation operators with moving averages
Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted mo...
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| Autor*in: |
Merigó, José M. [verfasserIn] Yager, Ronald R. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 23(2019), 21 vom: 03. Apr., Seite 10601-10615 |
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| Übergeordnetes Werk: |
volume:23 ; year:2019 ; number:21 ; day:03 ; month:04 ; pages:10601-10615 |
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| DOI / URN: |
10.1007/s00500-019-03892-w |
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| 520 | |a Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. | ||
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10.1007/s00500-019-03892-w doi (DE-627)SPR006508502 (SPR)s00500-019-03892-w-e DE-627 ger DE-627 rakwb eng Merigó, José M. verfasserin aut Aggregation operators with moving averages 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. Weighted average (dpeaa)DE-He213 OWA operator (dpeaa)DE-He213 Moving average (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Yager, Ronald R. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 21 vom: 03. Apr., Seite 10601-10615 (DE-627)SPR006469531 nnns volume:23 year:2019 number:21 day:03 month:04 pages:10601-10615 https://dx.doi.org/10.1007/s00500-019-03892-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 21 03 04 10601-10615 |
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10.1007/s00500-019-03892-w doi (DE-627)SPR006508502 (SPR)s00500-019-03892-w-e DE-627 ger DE-627 rakwb eng Merigó, José M. verfasserin aut Aggregation operators with moving averages 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. Weighted average (dpeaa)DE-He213 OWA operator (dpeaa)DE-He213 Moving average (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Yager, Ronald R. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 21 vom: 03. Apr., Seite 10601-10615 (DE-627)SPR006469531 nnns volume:23 year:2019 number:21 day:03 month:04 pages:10601-10615 https://dx.doi.org/10.1007/s00500-019-03892-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 21 03 04 10601-10615 |
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10.1007/s00500-019-03892-w doi (DE-627)SPR006508502 (SPR)s00500-019-03892-w-e DE-627 ger DE-627 rakwb eng Merigó, José M. verfasserin aut Aggregation operators with moving averages 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. Weighted average (dpeaa)DE-He213 OWA operator (dpeaa)DE-He213 Moving average (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Yager, Ronald R. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 21 vom: 03. Apr., Seite 10601-10615 (DE-627)SPR006469531 nnns volume:23 year:2019 number:21 day:03 month:04 pages:10601-10615 https://dx.doi.org/10.1007/s00500-019-03892-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 21 03 04 10601-10615 |
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10.1007/s00500-019-03892-w doi (DE-627)SPR006508502 (SPR)s00500-019-03892-w-e DE-627 ger DE-627 rakwb eng Merigó, José M. verfasserin aut Aggregation operators with moving averages 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. Weighted average (dpeaa)DE-He213 OWA operator (dpeaa)DE-He213 Moving average (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Yager, Ronald R. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 21 vom: 03. Apr., Seite 10601-10615 (DE-627)SPR006469531 nnns volume:23 year:2019 number:21 day:03 month:04 pages:10601-10615 https://dx.doi.org/10.1007/s00500-019-03892-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 21 03 04 10601-10615 |
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10.1007/s00500-019-03892-w doi (DE-627)SPR006508502 (SPR)s00500-019-03892-w-e DE-627 ger DE-627 rakwb eng Merigó, José M. verfasserin aut Aggregation operators with moving averages 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. Weighted average (dpeaa)DE-He213 OWA operator (dpeaa)DE-He213 Moving average (dpeaa)DE-He213 Aggregation operators (dpeaa)DE-He213 Yager, Ronald R. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 21 vom: 03. Apr., Seite 10601-10615 (DE-627)SPR006469531 nnns volume:23 year:2019 number:21 day:03 month:04 pages:10601-10615 https://dx.doi.org/10.1007/s00500-019-03892-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 21 03 04 10601-10615 |
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Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. |
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Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. |
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Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006508502</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002915.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-019-03892-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006508502</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-019-03892-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Merigó, José M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Aggregation operators with moving averages</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Weighted average</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">OWA operator</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Moving average</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aggregation operators</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yager, Ronald R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">23(2019), 21 vom: 03. Apr., Seite 10601-10615</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:21</subfield><subfield code="g">day:03</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:10601-10615</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-019-03892-w</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2019</subfield><subfield code="e">21</subfield><subfield code="b">03</subfield><subfield code="c">04</subfield><subfield code="h">10601-10615</subfield></datafield></record></collection>
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