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Results 21 - 30
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485
Frequency and time domain dynamic structural analysis: convergence and causality
, 2001
"... Matrix formulations for the dynamic analysis of SDOF systems in frequency and time domain are presented in this paper. The strict correspondence between both types of analysis are discussed. A study of the convergence of the re-sponse obtained through the frequency domain is performed. This study in ..."
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Matrix formulations for the dynamic analysis of SDOF systems in frequency and time domain are presented in this paper. The strict correspondence between both types of analysis are discussed. A study of the convergence of the re-sponse obtained through the frequency domain is performed. This study
Proceedings of the Institute of Acoustics SOUND FIELD VISUALIZATION USING THE FINITE- DIFFERENCE TIME-DOMAIN METHOD AND MEASURED SPATIAL ROOM IMPULSE RESPONSES
"... Visualization is an intuitive way to observe and analyse the phenomena related to the propagation of sound. It is evident that human hearing is highly capable of discriminating different aspects from an acoustic signal. The ability to discriminate is not necessarily enough to identify causality, tha ..."
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Visualization is an intuitive way to observe and analyse the phenomena related to the propagation of sound. It is evident that human hearing is highly capable of discriminating different aspects from an acoustic signal. The ability to discriminate is not necessarily enough to identify causality
Granger Causality’s Shortcomings and New Causality Measure
"... Abstract. Granger causality (GC) is one of the most popular measures to re-veal causality influence of time series and has been widely applied in economics and neuroscience due to its simplicity and easy implementation. In this paper, we show that GC in time domain cannot correctly determine how str ..."
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Abstract. Granger causality (GC) is one of the most popular measures to re-veal causality influence of time series and has been widely applied in economics and neuroscience due to its simplicity and easy implementation. In this paper, we show that GC in time domain cannot correctly determine how
More Discussions for Granger Causality and New Causality Measures
"... Abstract Granger causality (GC) has been widely ap-plied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al. 2011), we proposed new causalities in time and fre-quency domains and particularly focused on new causal-ity in frequency domain by po ..."
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Abstract Granger causality (GC) has been widely ap-plied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al. 2011), we proposed new causalities in time and fre-quency domains and particularly focused on new causal-ity in frequency domain
Representation and Control in Ixtet, a Temporal Planner
- In AIPS
, 1994
"... This paper presents a temporal planner, called IxTeT. It focuses on the representation and control issues, arguing for a compromise between the expressiveness and the ei~ciency of the search. The representation re-lies on a point-based reified logic, associated to mldti-valued domain attributes. Hie ..."
Abstract
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Cited by 146 (4 self)
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This paper presents a temporal planner, called IxTeT. It focuses on the representation and control issues, arguing for a compromise between the expressiveness and the ei~ciency of the search. The representation re-lies on a point-based reified logic, associated to mldti-valued domain attributes
On Lorentzian causality with continuous metrics
- Classical Quantum Gravity
"... Abstract. We present a systematic study of causality theory on Lorentzian manifolds with continuous metrics. Examples are given which show that some standard facts in smooth Lorentzian geometry, such as light-cones being hypersurfaces, are wrong when metrics which are merely continuous are considere ..."
Abstract
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Cited by 9 (0 self)
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are considered. We show that existence of time functions remains true on domains of dependence with continuous metrics, and that C0,1 differentiability of the metric suffices for many key results of the smooth causality theory.
Temporal Causal Modeling with Graphical Granger Methods
- In Proceedings of the 13th Int. Conference on Knowledge Discovery and Data Mining, 66 – 75: Association for Computing Machinery
, 2007
"... The need for mining causality, beyond mere statistical correlations, for real world problems has been recognized widely. Many of these applications naturally involve temporal data, which raises the challenge of how best to leverage the temporal information for causal modeling. Recently graphical mod ..."
Abstract
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Cited by 41 (6 self)
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modeling with the concept of “Granger causality”, based on the intuition that a cause helps predict its effects in the future, has gained attention in many domains involving time series data analysis. With the surge of interest in model selection methodologies for regression, such as the Lasso
2007): "Consumer Sentiment and Consumer Spending: Decomposing the Granger Causality Relationship in the Time Domain
- Applied Economics
"... It is often believed that the consumer sentiment index has predictive power for future consumption levels. While Granger causality tests have already been used to test for this, no attempt has been made yet to quantify the predictive power of the consumer sentiment index over different time horizons ..."
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Cited by 16 (1 self)
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It is often believed that the consumer sentiment index has predictive power for future consumption levels. While Granger causality tests have already been used to test for this, no attempt has been made yet to quantify the predictive power of the consumer sentiment index over different time
Causal Forces: Structuring Knowledge for Time-series Extrapolation
- JOURNAL OF FORECASTING
, 1993
"... This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, s ..."
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Cited by 36 (28 self)
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This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay
Multi-Dimensional Causal Discovery
- PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2013
"... We propose a method for learning causal relations within high-dimensional tensor data as they are typically recorded in non-experimental databases. The method allows the simultaneous inclusion of numerous dimensions within the data analysis such as samples, time and domain variables construed as ten ..."
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We propose a method for learning causal relations within high-dimensional tensor data as they are typically recorded in non-experimental databases. The method allows the simultaneous inclusion of numerous dimensions within the data analysis such as samples, time and domain variables construed
Results 21 - 30
of
485
