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Kernelbased Copula processes
 In ECML PKDD
, 2009
"... The field of timeseries analysis has made important contributions to a wide spectrum of applications such as tidelevel studies in hydrology, natural resource prospecting in geostatistics, speech recognition, weather forecasting, financial trading, and economic forecasts and analysis. Nevertheless ..."
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The field of timeseries analysis has made important contributions to a wide spectrum of applications such as tidelevel studies in hydrology, natural resource prospecting in geostatistics, speech recognition, weather forecasting, financial trading, and economic forecasts and analysis. Nevertheless, the analysis of the nonGaussian and nonstationary features of timeseries remains challenging for the current stateofart models. This thesis proposes an innovative framework that leverages the theory of copula, combined with a probabilistic framework from the machine learning community, to produce a versatile tool for multiple timeseries analysis. I coined this new model Kernelbased Copula Processes (KCPs). Under the new proposed framework, various idiosyncracies can be modeled compactly via a kernel function for each individual timeseries, and longrange dependency can be captured by a copula function. The copula function separates the marginal behavior and serial dependency structures, thus allowing them to be modeled separately and with much greater flexibility. Moreover, the codependent structure of a large number of timeseries with potentially vastly different characteristics can be captured in a compact and elegant fashion through the notion of a binding copula. This feature allows a highly heterogeneous model to be built,
Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors *
"... Abstract This paper puts forward a Bayesian version of the global vector autoregressive model (BGVAR) that accommodates international linkages across countries in a system of vector autoregressions. We compare the predictive performance of BGVAR models for the oneand fourquarter ahead forecast ..."
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Abstract This paper puts forward a Bayesian version of the global vector autoregressive model (BGVAR) that accommodates international linkages across countries in a system of vector autoregressions. We compare the predictive performance of BGVAR models for the oneand fourquarter ahead forecast horizon for standard macroeconomic variables (real GDP, inflation, the real exchange rate and interest rates). Our results show that taking international linkages into account improves forecasts of inflation, real GDP and the real exchange rate, while for interest rates forecasts of univariate benchmark models remain difficult to beat. Our Bayesian version of the GVAR model outperforms forecasts of the standard cointegrated VAR for practically all variables and at both forecast horizons. The comparison of prior elicitation strategies indicates that the use of the stochastic search variable selection (SSVS) prior tends to improve outofsample predictions systematically. This finding is confirmed by density forecast measures, for which the predictive ability of the SSVS prior is the best among all priors entertained for all variables at all forecasting horizons.
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"... The Working Paper series of the Oesterreichische Nationalbank is designed to disseminate and to provide a platform for discussion of either work of the staff of the OeNB economists or outside contributors on topics which are of special interest to the OeNB. To ensure the high quality of their conten ..."
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The Working Paper series of the Oesterreichische Nationalbank is designed to disseminate and to provide a platform for discussion of either work of the staff of the OeNB economists or outside contributors on topics which are of special interest to the OeNB. To ensure the high quality of their content, the contributions are subjected to an international refereeing process. The opinions are strictly those of the authors and do in no way commit the OeNB. The Working Papers are also available on our website
doi:10.5194/adgeo291092011 © Author(s) 2011. CC Attribution 3.0 License. Advances in
"... Reliability of autoregressive error models as postprocessors for probabilistic streamflow forecasts ..."
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Reliability of autoregressive error models as postprocessors for probabilistic streamflow forecasts