L1 Linear Interpolator of Missing Values in Time Series
We propose a minimum mean absolute error linear interpolator (MMAELI), based on the L1 approach. A linear functional of the observed time series due to non-normal innovations is derived. The solution equation for the coefficients of this linear functional is established in terms of the innovation se...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
Kluwer Academic Publishers
2003
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/44023 |
| _version_ | 1848756879033892864 |
|---|---|
| author | Lu, Zudi Hui, Y. |
| author_facet | Lu, Zudi Hui, Y. |
| author_sort | Lu, Zudi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We propose a minimum mean absolute error linear interpolator (MMAELI), based on the L1 approach. A linear functional of the observed time series due to non-normal innovations is derived. The solution equation for the coefficients of this linear functional is established in terms of the innovation series. It is found that information implied in the innovation series is useful for the interpolation of missing values. The MMAELIs of the AR(1) model with innovations following mixed normal and t distributions are studied in detail. The MMAELI also approximates the minimum mean squared error linear interpolator (MMSELI) well in mean squared error but outperforms the MMSELI in mean absolute error. An applicationto a real series is presented. Extensions to the general ARMA model and other time series models are discussed. |
| first_indexed | 2025-11-14T09:19:12Z |
| format | Journal Article |
| id | curtin-20.500.11937-44023 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:19:12Z |
| publishDate | 2003 |
| publisher | Kluwer Academic Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-440232017-09-13T16:05:30Z L1 Linear Interpolator of Missing Values in Time Series Lu, Zudi Hui, Y. Autoregressive process missing values minimum mean absolute error linear interpolation innovation departure We propose a minimum mean absolute error linear interpolator (MMAELI), based on the L1 approach. A linear functional of the observed time series due to non-normal innovations is derived. The solution equation for the coefficients of this linear functional is established in terms of the innovation series. It is found that information implied in the innovation series is useful for the interpolation of missing values. The MMAELIs of the AR(1) model with innovations following mixed normal and t distributions are studied in detail. The MMAELI also approximates the minimum mean squared error linear interpolator (MMSELI) well in mean squared error but outperforms the MMSELI in mean absolute error. An applicationto a real series is presented. Extensions to the general ARMA model and other time series models are discussed. 2003 Journal Article http://hdl.handle.net/20.500.11937/44023 10.1007/BF02530494 Kluwer Academic Publishers restricted |
| spellingShingle | Autoregressive process missing values minimum mean absolute error linear interpolation innovation departure Lu, Zudi Hui, Y. L1 Linear Interpolator of Missing Values in Time Series |
| title | L1 Linear Interpolator of Missing Values in Time Series |
| title_full | L1 Linear Interpolator of Missing Values in Time Series |
| title_fullStr | L1 Linear Interpolator of Missing Values in Time Series |
| title_full_unstemmed | L1 Linear Interpolator of Missing Values in Time Series |
| title_short | L1 Linear Interpolator of Missing Values in Time Series |
| title_sort | l1 linear interpolator of missing values in time series |
| topic | Autoregressive process missing values minimum mean absolute error linear interpolation innovation departure |
| url | http://hdl.handle.net/20.500.11937/44023 |