The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014) 2014
DOI: 10.1109/icsai.2014.7009332
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Restoration of missing time-series data via multiple sine functions decomposition with Guangzhou-temperature application

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Cited by 8 publications
(1 citation statement)
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“…1) is actually an upward part of a sine function with long cycle; and that some of the sine waveforms are consistent with the familiar temperature cycles, such as the annual cycle and the semi-annual cycle. Employing the MSFD method, we can restore missing data in the historical temperature sequence [12], as well as forecast the future temperature trend [13].…”
Section: Introductionmentioning
confidence: 99%
“…1) is actually an upward part of a sine function with long cycle; and that some of the sine waveforms are consistent with the familiar temperature cycles, such as the annual cycle and the semi-annual cycle. Employing the MSFD method, we can restore missing data in the historical temperature sequence [12], as well as forecast the future temperature trend [13].…”
Section: Introductionmentioning
confidence: 99%