Spectral analysis of unevenly spaced climatological time series

Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency...
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Autor*in:	Matyasovszky, István [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Ordinary Less Square Weighted Less Square Generalize Little Square Confidence Band Ordinary Less Square Estimate

Übergeordnetes Werk:	Enthalten in: Theoretical and applied climatology - Wien [u.a.] : Springer, 1948, 111(2012), 3-4 vom: 17. Mai, Seite 371-378
Übergeordnetes Werk:	volume:111 ; year:2012 ; number:3-4 ; day:17 ; month:05 ; pages:371-378

Links:	Volltext

DOI / URN:	10.1007/s00704-012-0669-z

Katalog-ID:	SPR007329571

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245	1	0	\|a Spectral analysis of unevenly spaced climatological time series
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520			\|a Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences.
650		4	\|a Ordinary Less Square \|7 (dpeaa)DE-He213
650		4	\|a Weighted Less Square \|7 (dpeaa)DE-He213
650		4	\|a Generalize Little Square \|7 (dpeaa)DE-He213
650		4	\|a Confidence Band \|7 (dpeaa)DE-He213
650		4	\|a Ordinary Less Square Estimate \|7 (dpeaa)DE-He213
773	0	8	\|i Enthalten in \|t Theoretical and applied climatology \|d Wien [u.a.] : Springer, 1948 \|g 111(2012), 3-4 vom: 17. Mai, Seite 371-378 \|w (DE-627)25490968X \|w (DE-600)1463177-5 \|x 1434-4483 \|7 nnns
773	1	8	\|g volume:111 \|g year:2012 \|g number:3-4 \|g day:17 \|g month:05 \|g pages:371-378
856	4	0	\|u https://dx.doi.org/10.1007/s00704-012-0669-z \|z lizenzpflichtig \|3 Volltext
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912			\|a GBV_ILN_31
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912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
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912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
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912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
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912			\|a GBV_ILN_4333
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912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
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allfields	10.1007/s00704-012-0669-z doi (DE-627)SPR007329571 (SPR)s00704-012-0669-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Matyasovszky, István verfasserin aut Spectral analysis of unevenly spaced climatological time series 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences. Ordinary Less Square (dpeaa)DE-He213 Weighted Less Square (dpeaa)DE-He213 Generalize Little Square (dpeaa)DE-He213 Confidence Band (dpeaa)DE-He213 Ordinary Less Square Estimate (dpeaa)DE-He213 Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 111(2012), 3-4 vom: 17. Mai, Seite 371-378 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:111 year:2012 number:3-4 day:17 month:05 pages:371-378 https://dx.doi.org/10.1007/s00704-012-0669-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 111 2012 3-4 17 05 371-378
spelling	10.1007/s00704-012-0669-z doi (DE-627)SPR007329571 (SPR)s00704-012-0669-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Matyasovszky, István verfasserin aut Spectral analysis of unevenly spaced climatological time series 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences. Ordinary Less Square (dpeaa)DE-He213 Weighted Less Square (dpeaa)DE-He213 Generalize Little Square (dpeaa)DE-He213 Confidence Band (dpeaa)DE-He213 Ordinary Less Square Estimate (dpeaa)DE-He213 Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 111(2012), 3-4 vom: 17. Mai, Seite 371-378 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:111 year:2012 number:3-4 day:17 month:05 pages:371-378 https://dx.doi.org/10.1007/s00704-012-0669-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 111 2012 3-4 17 05 371-378
allfields_unstemmed	10.1007/s00704-012-0669-z doi (DE-627)SPR007329571 (SPR)s00704-012-0669-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Matyasovszky, István verfasserin aut Spectral analysis of unevenly spaced climatological time series 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences. Ordinary Less Square (dpeaa)DE-He213 Weighted Less Square (dpeaa)DE-He213 Generalize Little Square (dpeaa)DE-He213 Confidence Band (dpeaa)DE-He213 Ordinary Less Square Estimate (dpeaa)DE-He213 Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 111(2012), 3-4 vom: 17. Mai, Seite 371-378 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:111 year:2012 number:3-4 day:17 month:05 pages:371-378 https://dx.doi.org/10.1007/s00704-012-0669-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 111 2012 3-4 17 05 371-378
allfieldsGer	10.1007/s00704-012-0669-z doi (DE-627)SPR007329571 (SPR)s00704-012-0669-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Matyasovszky, István verfasserin aut Spectral analysis of unevenly spaced climatological time series 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences. Ordinary Less Square (dpeaa)DE-He213 Weighted Less Square (dpeaa)DE-He213 Generalize Little Square (dpeaa)DE-He213 Confidence Band (dpeaa)DE-He213 Ordinary Less Square Estimate (dpeaa)DE-He213 Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 111(2012), 3-4 vom: 17. Mai, Seite 371-378 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:111 year:2012 number:3-4 day:17 month:05 pages:371-378 https://dx.doi.org/10.1007/s00704-012-0669-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 111 2012 3-4 17 05 371-378
allfieldsSound	10.1007/s00704-012-0669-z doi (DE-627)SPR007329571 (SPR)s00704-012-0669-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Matyasovszky, István verfasserin aut Spectral analysis of unevenly spaced climatological time series 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences. Ordinary Less Square (dpeaa)DE-He213 Weighted Less Square (dpeaa)DE-He213 Generalize Little Square (dpeaa)DE-He213 Confidence Band (dpeaa)DE-He213 Ordinary Less Square Estimate (dpeaa)DE-He213 Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 111(2012), 3-4 vom: 17. Mai, Seite 371-378 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:111 year:2012 number:3-4 day:17 month:05 pages:371-378 https://dx.doi.org/10.1007/s00704-012-0669-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 111 2012 3-4 17 05 371-378
language	English
source	Enthalten in Theoretical and applied climatology 111(2012), 3-4 vom: 17. Mai, Seite 371-378 volume:111 year:2012 number:3-4 day:17 month:05 pages:371-378
sourceStr	Enthalten in Theoretical and applied climatology 111(2012), 3-4 vom: 17. Mai, Seite 371-378 volume:111 year:2012 number:3-4 day:17 month:05 pages:371-378
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institution	findex.gbv.de
topic_facet	Ordinary Less Square Weighted Less Square Generalize Little Square Confidence Band Ordinary Less Square Estimate
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container_title	Theoretical and applied climatology
authorswithroles_txt_mv	Matyasovszky, István @@aut@@
publishDateDaySort_date	2012-05-17T00:00:00Z
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author	Matyasovszky, István
spellingShingle	Matyasovszky, István ddc 550 bkl 38.82 misc Ordinary Less Square misc Weighted Less Square misc Generalize Little Square misc Confidence Band misc Ordinary Less Square Estimate Spectral analysis of unevenly spaced climatological time series
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topic_title	550 ASE 38.82 bkl Spectral analysis of unevenly spaced climatological time series Ordinary Less Square (dpeaa)DE-He213 Weighted Less Square (dpeaa)DE-He213 Generalize Little Square (dpeaa)DE-He213 Confidence Band (dpeaa)DE-He213 Ordinary Less Square Estimate (dpeaa)DE-He213
topic	ddc 550 bkl 38.82 misc Ordinary Less Square misc Weighted Less Square misc Generalize Little Square misc Confidence Band misc Ordinary Less Square Estimate
topic_unstemmed	ddc 550 bkl 38.82 misc Ordinary Less Square misc Weighted Less Square misc Generalize Little Square misc Confidence Band misc Ordinary Less Square Estimate
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title	Spectral analysis of unevenly spaced climatological time series
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title_full	Spectral analysis of unevenly spaced climatological time series
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title_sort	spectral analysis of unevenly spaced climatological time series
title_auth	Spectral analysis of unevenly spaced climatological time series
abstract	Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences.
abstractGer	Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences.
abstract_unstemmed	Abstract Spectral analysis is often based on the periodogram and on fitting a first-order autoregressive [AR(1)] process to data as climatological time series generally exhibit red noise spectra that can be well approximated by AR(1) models. When the periodogram exceeds some threshold at a frequency, the spectrum is said to differ from the AR(1) spectrum, and the frequency is generally taken as a member of the discrete spectrum. This traditional technique, however, must not be used without modifications for unevenly spaced data. Our purpose is to provide an AR(1) modeling tool that is more accurate than the TAUEST procedure commonly used for unevenly spaced paleoclimatical records. A periodogram based on an entire least square fit to unevenly spaced data is also introduced instead of the well-known Lomb–Scargle periodogram. The methodology is applied to two paleoclimatological records. Our results compared to those of a frequently used procedure (consisting of TAUEST and Lomb–Scargle periodogram) show some interesting differences.
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title_short	Spectral analysis of unevenly spaced climatological time series
url	https://dx.doi.org/10.1007/s00704-012-0669-z
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doi_str	10.1007/s00704-012-0669-z
up_date	2024-07-04T02:51:07.725Z
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Spectral analysis of unevenly spaced climatological time series

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