Results 261 to 270 of about 174,276,385 (305)

Nocturnal cough as a syndromic surveillance signal for respiratory illness in England

Irons T, Carlsson E, Tang M, Mellor J, Rubin C, Allen A, Elliot AJ, Kågebäck M, Packham J.   +8 more
europepmc   +1 more source

Partial Autocorrelation Diagnostics for Count Time Series

Entropy, 2023
In a time series context, the study of the partial autocorrelation function (PACF) is helpful for model identification. Especially in the case of autoregressive (AR) models, it is widely used for order selection. During the last decades, the use of AR-type count processes, i.e., which also fulfil the Yule–Walker equations and thus provide the same PACF
Carsten Jentsch, Christian H. Weiß, Boris Aleksandrov   +2 more
exaly   +5 more sources

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SUPERPOSITIONED STATIONARY COUNT TIME SERIES

Probability in the Engineering and Informational Sciences, 2019
AbstractThis paper probabilistically explores a class of stationary count time series models built by superpositioning (or otherwise combining) independent copies of a binary stationary sequence of zeroes and ones. Superpositioning methods have proven useful in devising stationary count time series having prespecified marginal distributions.
Yisu Jia, Robert Lund, James Livsey
openaire   +3 more sources

On count time series prediction

Journal of Statistical Computation and Simulation, 2013
We consider the problem of assessing prediction for count time series based on either the Poisson distribution or the negative binomial distribution. By a suitable parametrization we employ both distributions with the same mean. We regress the mean on its past values and the values of the response and after obtaining consistent estimators of the ...
Christou, V., Fokianos, Konstantinos, Christou, V., Fokianos, Konstantinos   +3 more
openaire   +2 more sources

Changepoints in times series of counts

Journal of Time Series Analysis, 2012
In this article, we discuss the problem of testing for a changepoint in the structure of an integer‐valued time series. In particular, we consider a test statistic of cumulative sum type for general Poisson autoregressions of order 1. We investigate the asymptotic behaviour of conditional least‐squares estimates of the parameters in the presence of a ...
Franke, J., Kirch, C., Tadjuidje Kamgaing, J.   +2 more
openaire   +2 more sources

The Added Variable Plot for a Time Series of Counts

Australian & New Zealand Journal of Statistics, 1998
The issue of modelling non‐Gaussian time series data is one that has been examined by several authors in recent years. Zeger (1988) introduced a parameter‐driven model for a time series of counts as well as a more general observation‐driven model for non‐Gaussian data (Zeger & Qaqish, 1988). This paper examines the application of the added variable
Hay, J. L., Pettitt, A. N.
openaire   +1 more source

Extreme Overdispersion and Persistence in Time-Series of Counts

SSRN Electronic Journal, 2020
Time series of counts are often characterized by high overdispersion and persistence. These extreme features challenge the existing models. We approach this problem by combining the framework of INAR with a latent Markov structure. We call it HMM-INAR since it belongs to the class of hidden Markov models.
Catania, Leopoldo, Rossi, Eduardo, Santucci de Magistris, Paolo   +2 more
openaire   +2 more sources

Neighborhood counting for financial time series forecasting

2009 IEEE Congress on Evolutionary Computation, 2009
Time series data abound and analysis of such data is challenging and potentially rewarding. One example is financial time series analysis. Most of the intelligent data analysis methods can be applied in principle, but evolutionary computing is becoming increasingly popular and powerful.
Zhiwei Lin 0002, Yu Huang 0009, Hui Wang 0001, Sally I. McClean   +3 more
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Exact Bayesian designs for count time series

Computational Statistics & Data Analysis, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rakhi Singh, Siuli Mukhopadhyay
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Count Time Series Models

2012
Abstract We review regression models for count time series. We discuss the approach that is based on generalized linear models and the class of integer autoregressive processes. The generalized linear models' framework provides convenient tools for implementing model fitting and prediction using standard software. Furthermore, this approach provides a
Fokianos, Konstantinos, Fokianos, Konstantinos   +1 more
openaire   +2 more sources