Nichtparametrische Analyse Und Prognose Von Zeitreihen
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Any astronomer can predict just where every star will be at half past eleven tonight; he can make no such prediction about his daughter. James Truslow Adams, amerikanischer Historiker, 1878-1949 In der Zeitreihenanalyse spielt die Prognose zukiinftiger Werte eine wich- tige Rolle. Je nach Anwendungsgebiet kann sie als Planungs-und Steue- rungshilfe, als Indikator von Fehlentwicklungen, zur Friiherkennung und Vorwarnung, aber auch zur Spekulation herangezogen werden. Dariiber hinaus ermoglichen die meisten Prognosetechniken das Ersetzen fehlender Werte innerhalb des Beobachtungszeitraumes. Statistische Vorhersagever- fahren finden Verwendung in vielen wissenschaftlichen Disziplinen - so beispielsweise in den Wirtschafts- und Sozialwissenschaften, der Medizin, der Umweltforschung und der Hydrologie. Entstammen die beobachteten Zeitreihenwerte einem GauBprozeB, der durch Angabe seiner Mittelwert-und Kovarianzfunktion charakterisiert ist, so geniigt es, die klassischen Verfahren anzuwenden. Hangen diese Funk- tionen von endlich vielen Parametern ab, so konnen diese als Funktionen der Stichprobenautokovarianzen unter Regularitatsbedingungen asympto- tisch effizient geschatzt werden. Die Prognosen sind dann zumeist lineare Funktionen der zuletzt beobachteten Zeitreihenwerte. Einleitung 2 Die Normalitatsannahme ist ftir Zeitreihendaten vor allem dann frag- wiirdig, wenn der Verlauf der Reihe unregelmaf3ige Ausschlage aufweist, die sich von der Mehrzahl der Daten abheben. Wendet man ftir solche Zeitreihen dennoch ein auf der Normalitatsannahme beruhendes paramet- risches Verfahren an, so verlieren die Schatzungen in aller Regel ihre Effi- zienzeigenschaften. Da in der parametrischen Zeitreihenanalyse Prognosen iiblicherweise als Linearkombinationen einiger Werte vom Ende der Reihe berechnet werden, wobei die Schatzer als Koeffizienten eingehen, reduzie- ren schlechte Parameterschatzungen im allgemeinen die Prognosegiite.
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