نتایج جستجو برای: autoregressive integrated moving average arima

تعداد نتایج: 737312  

Journal: :East African journal of business and economics 2023

The Gross Domestic Product (GDP) is the total worth of all goods and services produced within a country's borders in given year. background study includes importance GDP as an important economic indicator reflecting overall performance growth country. As Somalia faces unique challenges, this research aims to provide insight into its dynamics, trends, potential future developments. In order crea...

Journal: :Indian Journal of Agricultural Sciences 2022

The objective of this study was to forecast the price natural rubber in India during April 2019 March 2020 by employing autoregressive integrated moving average (ARIMA). monthly pricing data for period from 2008 2018 used study. analysis carried out year 2018–19. RSS4 (Ribbed Smoked Sheets), latex (60% DRC (Dry Rubber Content)) and ISNR 20 (Indian Standard Natural Rubber) are different types In...

2006
Ming Zhong Satish Sharma Pawan Lingras

Previous research for short-term traffic prediction mostly forecasts only one time interval ahead. Such a methodology may not be adequate for response to emergency circumstances and road maintenance activities that last for a few hours or a longer period. In this study, various approaches, including naïve factor methods, exponential weighted moving average (EWMA), autoregressive integrated movi...

Journal: :PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 2019

2014
Paritosh K. Biswas Md. Zohorul Islam Nitish C. Debnath Mat Yamage

The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1) is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 ...

1999
Nuno Crato

Nonstationary ARIMA processes and nearly nonstationary ARMA processes, such as autoregressive processes having a root of the AR polynomial close to the unit circle, have sample autocovariance and spectral properties that are, in practice, almost indistinguishable from those of a stationary longmemory process, such as a Fractionally Integrated ARMA (ARFIMA) process. Because of this, model misspe...

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