{"title":"利用AMVAR和多锥度方法进行短窗光谱分析","authors":"N. Hariharan","doi":"10.1109/SPCOM.2004.1458364","DOIUrl":null,"url":null,"abstract":"In this paper, we compare two popular methods for estimating power spectrum for short time series, namely adaptive multivariate autoregressive (AMVAR) method and the multitaper method. By analyzing a simulated signal (embedded in a background Ornstein-Uhlenbeck noise process) we demonstrate that the AMVAR method performs better for very short data when compared to the multitaper method. We also show that coherence can still be detected in noisy bivariate time series data even if the individual power spectra fail to show any peaks.","PeriodicalId":424981,"journal":{"name":"2004 International Conference on Signal Processing and Communications, 2004. SPCOM '04.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Short-window spectral analysis using AMVAR and multitaper methods\",\"authors\":\"N. Hariharan\",\"doi\":\"10.1109/SPCOM.2004.1458364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we compare two popular methods for estimating power spectrum for short time series, namely adaptive multivariate autoregressive (AMVAR) method and the multitaper method. By analyzing a simulated signal (embedded in a background Ornstein-Uhlenbeck noise process) we demonstrate that the AMVAR method performs better for very short data when compared to the multitaper method. We also show that coherence can still be detected in noisy bivariate time series data even if the individual power spectra fail to show any peaks.\",\"PeriodicalId\":424981,\"journal\":{\"name\":\"2004 International Conference on Signal Processing and Communications, 2004. SPCOM '04.\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2004 International Conference on Signal Processing and Communications, 2004. SPCOM '04.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPCOM.2004.1458364\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 International Conference on Signal Processing and Communications, 2004. SPCOM '04.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPCOM.2004.1458364","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short-window spectral analysis using AMVAR and multitaper methods
In this paper, we compare two popular methods for estimating power spectrum for short time series, namely adaptive multivariate autoregressive (AMVAR) method and the multitaper method. By analyzing a simulated signal (embedded in a background Ornstein-Uhlenbeck noise process) we demonstrate that the AMVAR method performs better for very short data when compared to the multitaper method. We also show that coherence can still be detected in noisy bivariate time series data even if the individual power spectra fail to show any peaks.