We give a method for generation of periodically correlated and multivariate ARIMA models whose dynamic characteristics are partially or fully specified in terms of spectral poles and zeroes or their equivalents in the form of eigenvalues/eigenvectors of associated model matrices. Our method is based on the spectral decomposition of multi-companion matrices and their factorization into products of companion matrices. Generated models are needed in simulation but may also be used in estimation, e.g. to set sensible initial values of parameters for nonlinear optimization. Copyright 2009 The Authors. Journal compilation 2009 Blackwell Publishing Ltd
Breast Cancer forecasting is an important matter for governments, health sector investors, and other related companies. Although there are different forecasting models, choosing the suitable model is of great significance. This paper focuses on utilizing the performance of grey prediction model GM(1,1) to the monthly total number of women referrals for Breast Cancer in Gaza Strip\Palestine from January 2002 to December 2016. The results show that the GM(1,1) model exhibits good forecasting ability according to the MAPE criteria. Moreover, the forecasting results are compared with the results of exponential smoothing state space (ETS) and ARIMA models. The three techniques do similarly well in forecasting process. However, GM(1,1) outperforms the ETS and ARIMA techniques according to forecasting error accuracy measure MAPE.
Singular Spectrum Analysis (SSA) is a relatively new and powerful nonparametric tool for analyzing and forecasting economic data. SSA is capable of decomposing the main time series into independent components like trends, oscillatory manner and noise. This paper focuses on employing the performance of SSA approach to the monthly electricity consumption of the Middle Province in Gaza Strip\Palestine. The forecasting results are compared with the results of exponential smoothing state space (ETS) and ARIMA models. The three techniques do similarly well in forecasting process. However, SSA outperforms the ETS and ARIMA techniques according to forecasting error accuracy measures.
The maximum entropy problem for autocovariances given over a class of subsets of N is solved. A more general problem when prediction coefficients and prediction error variances are given instead of covariances is considered and solved, as well. Two notions about maximum entropy in time series context are introduced and some misconceptions in the literature are discussed.
The objective of the research is to estimate the month-ahead temperature records in Jerusalem, Palestine. In this study, the modeling mechanism of analytic for forecasting is considered. This paper explores implementation of ARIMA and GARCH modeling techniques to fit a historical data set and estimate the coefficients of the suitable models for fitting the average monthly temperature data of Jerusalem in Palestine for the period from January 1964 to December 2013. The analysis of this study are carried out with the assist of R software. Eventually, using different statistical measures,comparison efficiency between ARIMA$\ (2,0,1)(2,1,1)_{12}$ and AR(1)-GARCH(1,1) models are produced. AR(1)-GARCH(1,1) is detected to be a better than ARIMA$\ (2,0,1)(2,1,1)_{12}$ model.
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