Algorithms for Linear Time Series Analysis: With R Package
Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2007-11-01
|
| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v23/i05/paper |
| _version_ | 1818267022433189888 |
|---|---|
| author | A. Ian McLeod Hao Yu Zinovi L. Krougly |
| author_facet | A. Ian McLeod Hao Yu Zinovi L. Krougly |
| author_sort | A. Ian McLeod |
| collection | DOAJ |
| description | Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software. |
| first_indexed | 2024-12-12T20:15:59Z |
| format | Article |
| id | doaj.art-4a45c47cf71c40a8aa436941db21f5b9 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-12T20:15:59Z |
| publishDate | 2007-11-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-4a45c47cf71c40a8aa436941db21f5b92022-12-22T00:13:22ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602007-11-01235Algorithms for Linear Time Series Analysis: With R PackageA. Ian McLeodHao YuZinovi L. KrouglyOur ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software.http://www.jstatsoft.org/v23/i05/paperexact maximum likelihood estimationforecastingfractional Gaussian noisein- verse symmetric Toeplitz matrixlong memory and the Nile river minimatime series regressiontime series simulation |
| spellingShingle | A. Ian McLeod Hao Yu Zinovi L. Krougly Algorithms for Linear Time Series Analysis: With R Package Journal of Statistical Software exact maximum likelihood estimation forecasting fractional Gaussian noise in- verse symmetric Toeplitz matrix long memory and the Nile river minima time series regression time series simulation |
| title | Algorithms for Linear Time Series Analysis: With R Package |
| title_full | Algorithms for Linear Time Series Analysis: With R Package |
| title_fullStr | Algorithms for Linear Time Series Analysis: With R Package |
| title_full_unstemmed | Algorithms for Linear Time Series Analysis: With R Package |
| title_short | Algorithms for Linear Time Series Analysis: With R Package |
| title_sort | algorithms for linear time series analysis with r package |
| topic | exact maximum likelihood estimation forecasting fractional Gaussian noise in- verse symmetric Toeplitz matrix long memory and the Nile river minima time series regression time series simulation |
| url | http://www.jstatsoft.org/v23/i05/paper |
| work_keys_str_mv | AT aianmcleod algorithmsforlineartimeseriesanalysiswithrpackage AT haoyu algorithmsforlineartimeseriesanalysiswithrpackage AT zinovilkrougly algorithmsforlineartimeseriesanalysiswithrpackage |