Multiple Time Series (multiple + time_series)

Distribution by Scientific Domains

Selected Abstracts

Statistical methods for regular monitoring data

Michael L. Stein
Summary., Meteorological and environmental data that are collected at regular time intervals on a fixed monitoring network can be usefully studied combining ideas from multiple time series and spatial statistics, particularly when there are little or no missing data. This work investigates methods for modelling such data and ways of approximating the associated likelihood functions. Models for processes on the sphere crossed with time are emphasized, especially models that are not fully symmetric in space,time. Two approaches to obtaining such models are described. The first is to consider a rotated version of fully symmetric models for which we have explicit expressions for the covariance function. The second is based on a representation of space,time covariance functions that is spectral in just the time domain and is shown to lead to natural partially nonparametric asymmetric models on the sphere crossed with time. Various models are applied to a data set of daily winds at 11 sites in Ireland over 18 years. Spectral and space,time domain diagnostic procedures are used to assess the quality of the fits. The spectral-in-time modelling approach is shown to yield a good fit to many properties of the data and can be applied in a routine fashion relative to finding elaborate parametric models that describe the space,time dependences of the data about as well. [source]

On The Peņa,Box Model

Yu-Pin Hu
Abstract., Peņa and Box [Journal of Americal Statistical Association (1987) Vol. 82, PP. 836,843] proposed a factor model which aimed to explore the possibility of using lower-dimensional series to represent or explain an observed higher-dimensional multiple time series. However, there were no statistics with distribution results with which to build the model. In this paper, we derive a statistical procedure to build the model for stationary and first-order non-stationary series. The main idea, conducted by the canonical correlation analysis between present series and non-present series, is an extension of the concept of the scalar component model proposed by Tiao and Tsay [Journal of the Royal Statistical Society B (1989) Vol. 51, pp. 157,213]. Finally, simulation studies and reanalysis of two real data sets are illustrated. [source]

Identifying environmental signals from population abundance data using multivariate time-series analysis

OIKOS, Issue 11 2009
Masami Fujiwara
Individual organisms are affected by various natural and anthropogenic environmental factors throughout their life history. This is reflected in the way population abundance fluctuates. Consequently, observed population dynamics are often produced by the superimposition of multiple environmental signals. This complicates the analysis of population time-series. Here, a multivariate time-series method called maximum autocorrelation factor analysis (MAFA) was used to extract underlying signals from multiple population time series data. The extracted signals were compared with environmental variables that were suspected to affect the populations. Finally, a simple multiple regression analysis was applied to the same data set, and the results from the regression analysis were compared with those from MAFA. The extracted signals with MAFA were strongly associated with the environmental variables, suggesting that they represent environmental factors. On the other hand, with the multiple regression analysis, one of the important signals was not identifiable, revealing the shortcoming of the conventional approach. MAFA summarizes data based on their lag-one autocorrelation. This allows the identification of underlying signals with a small effect size on population abundance during the observation. It also uses multiple time series collected in parallel; this enables us to effectively analyze short time series. In this study, annual spawning adult counts of Chinook salmon at various locations within the Klamath Basin, California, were analyzed. [source]

Permanent-transitory Decomposition in Var Models With Cointegration and Common Cycles

Alain Hecq
In this paper we derive permanent-transitory decompositions of non-stationary multiple times series generated by (r)nite order Gaussian VAR(p) models with both cointegration and serial correlation common features. We extend existing analyses to the two classes of reduced rank structures discussed in Hecq, Palm and Urbain (1998). Using the corresponding state space representation of cointegrated VAR models in vector error correction form we show how decomposition can be obtained even in the case where the number of common feature and cointegration vectors are not equal to the number of variables. As empirical analysis of US business fluctuations shows the practical relevance of the approach we propose. [source]