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ARMA Model for Revenue Prediction
Proceedings of the 11th International Conference on Advances in Information Technology, 2020For every country in over the world, tax revenues appear to be the main engines contributing to the growth momentum. The prediction of tax revenues is one of the main challenges of the Myanmar Internal Revenue Department. It is not easy to get an accurate prediction of the tax revenues of the coming financial year.
Thura Zaw, Swe Swe Kyaw, Aung Nway Oo
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Arma models with bilinear innovations
Communications in Statistics. Stochastic Models, 1999Summary: It is well known that any purely non-deterministic stationary process \((X_t)\) with finite variance can be written as an infinite moving average in terms of its innovation process. This property is widely used in the linear methods of estimation and prediction of time series but these methods may give poor results when the innovations are not
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ESTIMATION OF SPATIAL ARMA MODELS
Australian Journal of Statistics, 1992SummarySpatial ARMA models are considered using the nonsymmetric half plane ordering on a lattice of data. A method is given for the estimation of the orders and the coefficients of such models under an identifiability condition and the condition that the beat linear predictor is the best predictor in the mean square sense.
Huang, D., Anh, V. V.
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State representations of ARMA-models
International Journal of Control, 2010A state representation of an arbitrary ARMA-model is computed explicitly. It is shown then that every ARMA-model is homotopy equivalent to its state representation, and that two state models are homotopy equivalent if and only if they are similar.
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Modelling and Forecasting with ARMA Processes
1996The determination of an appropriate ARMA(p, q) model to represent an observed stationary time series involves a number of interrelated problems. These include the choice of p and q (order selection) and estimation of the mean, the coefficients {ϕ i , i = 1, …, p}, {θ i , i = 1, …, q}, and the white noise variance σ2.
Peter J. Brockwell, Richard A. Davis
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Journal of Time Series Analysis, 1984
Abstract.This paper considers the class of ARMA models with ARCH errors. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and incorporated into techniques for model building based upon the application of the usual Box‐Jenkins methodology of identification, estimation and diagnostic ...
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Abstract.This paper considers the class of ARMA models with ARCH errors. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and incorporated into techniques for model building based upon the application of the usual Box‐Jenkins methodology of identification, estimation and diagnostic ...
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ARMA MODELLING WITH NON‐GAUSSIAN INNOVATIONS
Journal of Time Series Analysis, 1988Abstract.The problem of modelling time series driven by non‐Gaussian innovations is considered. The asymptotic normality of the maximum likelihood estimator is established under some general conditions. The distribution of the residual autocorrelations is also obtained. This gives rise to a potentially useful goodness‐of‐fit statistic.
McLeod, AI, Li, WK
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The quality of models for ARMA processes
IEEE Transactions on Signal Processing, 1998The model error (ME) is an objective measure for assessing the quality of different models of a given ARMA process. The expression for ME can be evaluated easily in the time domain. This quality measure for known and given processes is necessary for an objective comparison of the performance of estimation algorithms and of order selection criteria.
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