Spatial time-series modeling with R system
In this paper we propose modeling technique, which was applied to multivariate time series data that correspond to different spatial locations (spatial time series). ARIMA model class is considered for each location. Forecasting model for new location is built by spatial "connection" of i...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Vilnius University Press
2004-12-01
|
| Series: | Lietuvos Matematikos Rinkinys |
| Subjects: | |
| Online Access: | https://www.journals.vu.lt/LMR/article/view/32262 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593224880881664 |
|---|---|
| author | Laura Šaltytė Kęstutis Dučinskas |
| author_facet | Laura Šaltytė Kęstutis Dučinskas |
| author_sort | Laura Šaltytė |
| collection | DOAJ |
| description |
In this paper we propose modeling technique, which was applied to multivariate time series data that correspond to different spatial locations (spatial time series). ARIMA model class is considered for each location. Forecasting model for new location is built by spatial "connection" of identified models in observed locations. Spatial "connection" is implemented by spatial averaging of the coefficients of models and by ordinary kriging procedure for means. This modeling technique is illustrated by a substantive example using R system.
|
| format | Article |
| id | doaj-art-d3d1db9d73174ba7afa23b996bb4a084 |
| institution | Kabale University |
| issn | 0132-2818 2335-898X |
| language | English |
| publishDate | 2004-12-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj-art-d3d1db9d73174ba7afa23b996bb4a0842025-01-20T18:16:18ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2004-12-0144spec.10.15388/LMR.2004.32262Spatial time-series modeling with R systemLaura Šaltytė0Kęstutis Dučinskas1Klaipedos UniversityKlaipedos University In this paper we propose modeling technique, which was applied to multivariate time series data that correspond to different spatial locations (spatial time series). ARIMA model class is considered for each location. Forecasting model for new location is built by spatial "connection" of identified models in observed locations. Spatial "connection" is implemented by spatial averaging of the coefficients of models and by ordinary kriging procedure for means. This modeling technique is illustrated by a substantive example using R system. https://www.journals.vu.lt/LMR/article/view/32262spatial time series modelingARIMAkrigingsemivariogram |
| spellingShingle | Laura Šaltytė Kęstutis Dučinskas Spatial time-series modeling with R system Lietuvos Matematikos Rinkinys spatial time series modeling ARIMA kriging semivariogram |
| title | Spatial time-series modeling with R system |
| title_full | Spatial time-series modeling with R system |
| title_fullStr | Spatial time-series modeling with R system |
| title_full_unstemmed | Spatial time-series modeling with R system |
| title_short | Spatial time-series modeling with R system |
| title_sort | spatial time series modeling with r system |
| topic | spatial time series modeling ARIMA kriging semivariogram |
| url | https://www.journals.vu.lt/LMR/article/view/32262 |
| work_keys_str_mv | AT laurasaltyte spatialtimeseriesmodelingwithrsystem AT kestutisducinskas spatialtimeseriesmodelingwithrsystem |