Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
James McCracken received his B.S. in Physics and B.S. in Astrophysics from the Florida Institute of Technology in 2004, his M.S. from the University of Central Florida in 2006, and his Ph.D. in Physics from George Mason University in 2015. He currently lives and works in the Washington, D.C., metro area.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
EUR 16,94 für den Versand von USA nach Deutschland
Versandziele, Kosten & DauerGratis für den Versand innerhalb von/der Deutschland
Versandziele, Kosten & DauerAnbieter: moluna, Greven, Deutschland
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. James McCracken received his B.S. in Physics and B.S. in Astrophysics from the Florida Institute of Technology in 2004, his M.S. from the University of Central Florida in 2006, and his Ph.D. in Physics from George Mason University in 2015. He currently live. Bestandsnummer des Verkäufers 608129167
Anzahl: Mehr als 20 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise. Bestandsnummer des Verkäufers 9783031007811
Anzahl: 1 verfügbar
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. Neuware -Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 148 pp. Englisch. Bestandsnummer des Verkäufers 9783031007811
Anzahl: 2 verfügbar
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise. 148 pp. Englisch. Bestandsnummer des Verkäufers 9783031007811
Anzahl: 2 verfügbar
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In. Bestandsnummer des Verkäufers ria9783031007811_new
Anzahl: Mehr als 20 verfügbar
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New. Bestandsnummer des Verkäufers 44571012-n
Anzahl: Mehr als 20 verfügbar
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. PRINT ON DEMAND. Bestandsnummer des Verkäufers 18395061307
Anzahl: 4 verfügbar
Anbieter: Best Price, Torrance, CA, USA
Zustand: New. SUPER FAST SHIPPING. Bestandsnummer des Verkäufers 9783031007811
Anzahl: 2 verfügbar
Anbieter: Books Puddle, New York, NY, USA
Zustand: New. 1st edition NO-PA16APR2015-KAP. Bestandsnummer des Verkäufers 26395061297
Anzahl: 4 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: New. Bestandsnummer des Verkäufers 44571012-n
Anzahl: Mehr als 20 verfügbar