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Multivariate Time Series (multivariate + time_series)
Selected AbstractsELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORKAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 3 2007Catriona M. Queen Summary The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM. [source] Estimating common trends in multivariate time series using dynamic factor analysisENVIRONMETRICS, Issue 7 2003A. F. Zuur Abstract This article discusses dynamic factor analysis, a technique for estimating common trends in multivariate time series. Unlike more common time series techniques such as spectral analysis and ARIMA models, dynamic factor analysis can analyse short, non-stationary time series containing missing values. Typically, the parameters in dynamic factor analysis are estimated by direct optimization, which means that only small data sets can be analysed if computing time is not to become prohibitively long and the chances of obtaining sub-optimal estimates are to be avoided. This article shows how the parameters of dynamic factor analysis can be estimated using the EM algorithm, allowing larger data sets to be analysed. The technique is illustrated on a marine environmental data set. Copyright © 2003 John Wiley & Sons, Ltd. [source] Evolutionary learning of dynamic probabilistic models with large time lagsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2001Allan Tucker In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc. [source] Forging Democracy at GunpointINTERNATIONAL STUDIES QUARTERLY, Issue 3 2006JEFFREY PICKERING Can liberal interventionism build liberal democracy? This manuscript examines the military interventions undertaken by the U.S., U.K., France, and the UN in the post-World War II era to see if they had a positive impact on democracy in target countries. Empirical analysis centers on multivariate time series, cross section PCSE and relogit regressions of political liberalization and democratization from 1946 to 1996. The former is operationalized with annual difference data drawn from the Polity IV data collection, whereas the latter is a binary variable denoting countries that cross a threshold commonly used to indicate the establishment of democratic institutions. An updated version of the International Military Intervention data set enumerates foreign military interventions. We find little evidence that military intervention by liberal states helps to foster democracy in target countries. Although a few states have democratized in the wake of hostile U.S. military interventions, the small number of cases involved makes it difficult to draw generalizable conclusions from the U.S. record. We find stronger evidence, however, that supportive interventions by the UN's "Blue Helmets" can help to democratize target states. [source] Forecast covariances in the linear multiregression dynamic modelJOURNAL OF FORECASTING, Issue 2 2008Catriona M. Queen Abstract The linear multiregression dynamic model (LMDM) is a Bayesian dynamic model which preserves any conditional independence and causal structure across a multivariate time series. The conditional independence structure is used to model the multivariate series by separate (conditional) univariate dynamic linear models, where each series has contemporaneous variables as regressors in its model. Calculating the forecast covariance matrix (which is required for calculating forecast variances in the LMDM) is not always straightforward in its current formulation. In this paper we introduce a simple algebraic form for calculating LMDM forecast covariances. Calculation of the covariance between model regression components can also be useful and we shall present a simple algebraic method for calculating these component covariances. In the LMDM formulation, certain pairs of series are constrained to have zero forecast covariance. We shall also introduce a possible method to relax this restriction. Copyright © 2008 John Wiley & Sons, Ltd. [source] Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approachJOURNAL OF FORECASTING, Issue 1 2007Fabio H. Nieto Abstract A methodology for estimating high-frequency values of an unobserved multivariate time series from low-frequency values of and related information to it is presented in this paper. This is an optimal solution, in the multivariate setting, to the problem of ex post prediction, disaggregation, benchmarking or signal extraction of an unobservable stochastic process. Also, the problem of extrapolation or ex ante prediction is optimally solved and, in this context, statistical tests are developed for checking online the ocurrence of extreme values of the unobserved time series and consistency of future benchmarks with the present and past observed information. The procedure is based on structural or unobserved component models, whose assumptions and specification are validated with the data alone.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] A dynamic principal components analysis based on multivariate matrix normal dynamic linear modelsJOURNAL OF FORECASTING, Issue 6-7 2003Manuel Salvador Abstract In this paper, we propose a multivariate dynamic linear model (MDLM) that allows us to carry out a dynamic principal components analysis in a set of multivariate time series and to analyse the similarity in their evolution once the influence of non-stationarity in each of them has been removed. In order to illustrate the methodology, we consider the distribution of value added of the firms operating in the Spanish Transport Material Manufacturing sector.,Copyright © 2003 John Wiley & Sons, Ltd. [source] Forecasting multivariate time series with linear restrictions using constrained structural state-space modelsJOURNAL OF FORECASTING, Issue 4 2002Gurupdesh S. Pandher Abstract This paper presents a methodology for modelling and forecasting multivariate time series with linear restrictions using the constrained structural state-space framework. The model has natural applications to forecasting time series of macroeconomic/financial identities and accounts. The explicit modelling of the constraints ensures that model parameters dynamically satisfy the restrictions among items of the series, leading to more accurate and internally consistent forecasts. It is shown that the constrained model offers superior forecasting efficiency. A testable identification condition for state space models is also obtained and applied to establish the identifiability of the constrained model. The proposed methods are illustrated on Germany's quarterly monetary accounts data. Results show significant improvement in the predictive efficiency of forecast estimators for the monetary account with an overall efficiency gain of 25% over unconstrained modelling. Copyright © 2002 John Wiley & Sons, Ltd. [source] A Generalized Portmanteau Test For Independence Of Two Infinite-Order Vector Autoregressive SeriesJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2006Chafik Bouhaddioui Primary 62M10; secondary 62M15 Abstract., In many situations, we want to verify the existence of a relationship between multivariate time series. Here, we propose a semiparametric approach for testing the independence between two infinite-order vector autoregressive (VAR(,)) series, which is an extension of Hong's [Biometrika (1996c) vol. 83, 615,625] univariate results. We first filter each series by a finite-order autoregression and the test statistic is a standardized version of a weighted sum of quadratic forms in the residual cross-correlation matrices at all possible lags. The weights depend on a kernel function and on a truncation parameter. Using a result of Lewis and Reinsel [Journal of Multivariate Analysis (1985) Vol. 16, pp. 393,411], the asymptotic distribution of the test statistic is derived under the null hypothesis and its consistency is also established for a fixed alternative of serial cross-correlation of unknown form. Apart from standardization factors, the multivariate portmanteau statistic proposed by Bouhaddioui and Roy [Statistics and Probability Letters (2006) vol. 76, pp. 58,68] that takes into account a fixed number of lags can be viewed as a special case by using the truncated uniform kernel. However, many kernels lead to a greater power, as shown in an asymptotic power analysis and by a small simulation study in finite samples. A numerical example with real data is also presented. [source] ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORKAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 3 2007Catriona M. Queen Summary The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM. [source] | ||||||||||