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Showing 4 results for Time Series
Behzad Mansouri, Rahim Chinipardaz,
Volume 12, Issue 2 (3-2019)
Abstract
In this paper, using Band matrix, a method has been proposed to estimating the covariance matrix of the ARMA model and the likelihood function of the ARMA model with diagonal covariance matrix has been obtained and approximations for Kullback-Leibler and Chernoff criteria were presented. In addition, two rules for discriminating the ARMA models has been proposed. A simulation and real data sets are used to illustrate the performance of the proposed rules. Significant reduction of the calculations for large time series and low discrimination error rate are two characteristics of the proposed rules. In addition no need to normal assumption is showed in a theorem.
Emad Ashtari Nezhad, Yadollah Waghei, Gholam Reza Mohtashami Borzadaran, Hamid Reza Nili Sani, Hadi Alizadeh Noughabi,
Volume 13, Issue 1 (9-2019)
Abstract
Before analyzing a time series data, it is better to verify the dependency of the data, because if the data be independent, the fitting of the time series model is not efficient. In recent years, the power divergence statistics used for the goodness of fit test. In this paper, we introduce an independence test of time series via power divergence which depends on the parameter λ. We obtain asymptotic distribution of the test statistic. Also using a simulation study, we estimate the error type I and test power for some λ and n. Our simulation study shows that for extremely large sample sizes, the estimated error type I converges to the nominal α, for any λ. Furthermore, the modified chi-square, modified likelihood ratio, and Freeman-Tukey test have the most power.
Najmeh Rezaeerad, Mahnaz Khalafi, Mohsen Hoseinalizadeh, Majid Azimmohseni,
Volume 17, Issue 2 (2-2024)
Abstract
The analysis of spatio-temporal series is crucial but a challenge in different sciences. Accurate analyses of spatio-temporal series depend on how to measure their spatial and temporal relation simultaneously. In this article, one-sided dynamic principal components (ODPC) for spatio-temporal series are introduced and used to model the common structure of their relation. These principal components can be used in the data set, including many spatio-temporal series. In addition to spatial relations, trends, and seasonal trends, the dynamic principal components reflect other common temporal and spatial factors in spatio-temporal series. In order to evaluate the capability of one-sided dynamic principal components, they are used for clustering and forecasting in spatio-temporal series. Based on the precipitation time series in different stations of Golestan province, the efficiency of the principal components in the clustering of hydrometric stations is investigated. Moreover, forecasting for the SPI index, an essential indicator for detecting drought, is conducted based on the one-sided principal components.
Arezu Rahmanpour, Yadollah Waghei, Gholam Reza Mohtashami Borzadaran,
Volume 19, Issue 1 (9-2025)
Abstract
Change point detection is one of the most challenging statistical problems because the number and position of these points are unknown. In this article, we will first introduce the concept of change point and then obtain the parameter estimation of the first-order autoregressive model AR(1); in order to investigate the precision of estimated parameters, we have done a simulation study. The precision and consistency of parameters were evaluated using MSE. The simulation study shows that parameter estimation is consistent. In the sense that as the sample size increases, the MSE of different parameters converges to zero. Next, the AR(1) model with the change point was fitted to Iran's annual inflation rate data (from 1944 to 2022), and the inflation rate in 2023 and 2024 was predicted using it.
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