Arbeitspapier
Filtering the noise from time series and spatial data
Noisy observations form the basis for almost every scientific research and especially in environmental monitoring. The Noise is often an effect of imprecise instruments which cause measurement errors. If the noise variance is known it is possible to filter out the contaminating noise from the observations and then to predict the latent signal process. Solutions for this problem exist for time series application and will be briefly reviewed. In the geostatistical literature, i.e. for the analysis of spatial data, similar methods have been foreshadowed in the literature and will be outlined in this work.
- Sprache
-
Englisch
- Erschienen in
-
Series: Technical Report ; No. 1998,18
- Thema
-
Geostatistics
Kalman Filter
Kriging
Prediction
Signal
Time Series Analysis
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Berke, Olaf
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (wo)
-
Dortmund
- (wann)
-
1998
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Berke, Olaf
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Entstanden
- 1998
Ähnliche Objekte (12)
Filtering the Noise from Time Series and Spatial Data
Time series modeling and filtering method of electric power load stochastic noise
Spatial noise filtering through error correction for quantum sensing
Robust filtering of time series with trends
Robust Filtering of Time Series with Trends
Noise filtering for big data analytics
On the robust detection of edges in time series filtering
On the robust detection of edges in time series filtering
Environmental noise event classification based on self-organizing map using psychoacoustic features and spatial filtering
Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering
Singular value decomposition of noisy data: noise filtering
In‐Band Noise Filtering via Spatio‐Spectral Coupling
Filtering the Noise from Time Series and Spatial Data
Time series modeling and filtering method of electric power load stochastic noise
Spatial noise filtering through error correction for quantum sensing
Robust filtering of time series with trends
Robust Filtering of Time Series with Trends
Noise filtering for big data analytics
On the robust detection of edges in time series filtering
On the robust detection of edges in time series filtering
Environmental noise event classification based on self-organizing map using psychoacoustic features and spatial filtering
Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering
Singular value decomposition of noisy data: noise filtering
In‐Band Noise Filtering via Spatio‐Spectral Coupling
Filtering the Noise from Time Series and Spatial Data
Time series modeling and filtering method of electric power load stochastic noise
Spatial noise filtering through error correction for quantum sensing
Robust filtering of time series with trends
Robust Filtering of Time Series with Trends
Noise filtering for big data analytics
On the robust detection of edges in time series filtering
On the robust detection of edges in time series filtering
Environmental noise event classification based on self-organizing map using psychoacoustic features and spatial filtering
Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering
Singular value decomposition of noisy data: noise filtering