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Spatiotemporal predictions using an MSSA approach / Richard Opaka Awichi
VerfasserAwichi, Richard Opaka
Begutachter / BegutachterinMüller, Werner Günther ; Grün, Bettina
UmfangXII, 107 Bl. : graph. Darst.
HochschulschriftLinz, Univ., Diss., 2015
Bibl. ReferenzOeBB
Schlagwörter (DE)Zeitreihenanalyse / multivariate Singulär-Spektrum-Analyse / inverse Distanzgewichtung / räumliche Abhängigkeit
Schlagwörter (EN)time series analysis / multivariate singular spectrum analysis / inverse distance weighting / spatial dependence
Schlagwörter (GND)Statistik / Zeitreihenanalyse / Frequenzanalyse / Multivariate Analyse / Raum / Distanz / Inversion <Mathematik>
URNurn:nbn:at:at-ubl:1-2751 Persistent Identifier (URN)
 Das Werk ist gemäß den "Hinweisen für BenützerInnen" verfügbar
Spatiotemporal predictions using an MSSA approach [1.62 mb]
Zusammenfassung (Deutsch)

In this thesis, a method for utilizing the usually intrinsic spatial information in spatial data sets to improve the quality of temporal predictions within the framework of singular spectrum analysis (SSA) techniques is presented. The SSA-based techniques constitute a model free approach to time series analysis and literally SSA can be applied to any time series with a notable structure. Indeed it has a wide area of application including social sciences, medical sciences, finance, environmental sciences, mathematics, dynamical systems and economics.

Multivariate singular spectrum analysis (MSSA) is an extension of SSA to multivariate statistics and takes advantage of the delay procedure to obtain a similar formulation as SSA though with larger matrices for multivariate data. In situations where spatial data is an important focus of investigation, it is not uncommon to have attributes whose values change with space and time and an accurate prediction is thus important. The usual question asked is whether the intrinsic location parameters in spatial data can improve data analysis of such data sets.

Results show that the proposed technique of incorporating spatial dependence into MSSA analysis leads to improved quality of statistical inference.