نتایج جستجو برای: مدل اقتصادسنجی arima

تعداد نتایج: 123637  

ژورنال: :پژوهشنامه اقتصادی 0

از جمله مباحث بسیار مهم در ادبیات اقتصادی، بررسی تأثیر ساختار مالی بر رشد اقتصادی است. به رغم تحقیقات متعددی که درباره کشورهای مختلف با ساختارهای مالی متفاوت انجام شده، تا کنون اجماع نظری در باره تأثیر آن بر رشد اقتصادی بدست نیامده است. در این تحقیق با استفاده از روشهای اقتصادسنجی مرسوم، رابطه میان ساختارمالی و رشد اقتصادی ایران در دوره 1370- 1384 مورد آزمون قرار گرفت.نتایج بدست آمده نشان داد ک...

2007
Viviana Fernandez

In this article, we forecast crude oil and natural gas spot prices at a daily frequency based on two classification techniques: artificial neural networks (ANN) and support vector machines (SVM). As a benchmark, we utilize an autoregressive integrated moving average (ARIMA) specification. We evaluate outof-sample forecast based on encompassing tests and mean-squared prediction error (MSPE). We ...

1998
Marwan Krunz Armand Makowski

Statistical evidence suggests that the autocorrelation function of a compressed-video sequence is better captured by p(k) = e–~fi than by p(k) = k–fi = e–~’og k (long-range dependence) or p(k) = e-~k (Markovian). A video model with such a correlation structure is introduced based on the so-called M/G/ca input processes. Though not Markovian, the model exhibits short-range dependence. Using the ...

Journal: :Chemosphere 2005
C Dueñas M C Fernández S Cañete J Carretero E Liger

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,...

1998
AIDAN MEYLER GEOFF KENNY TERRY QUINN

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models the Box Jenkins approach and the objective penalty function methods. The emphasis is on forec...

Journal: :Technometrics : a journal of statistics for the physical, chemical, and engineering sciences 2005
A. Ian McLeod E. R. Vingilis

In many intervention analysis applications, time series data may be expensive or otherwise difficult to collect. In this case the power function is helpful, because it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming that an underlying autoregressive integrated moving average (ARIMA) or fractional ARIMA model is...

باتوجه به کاهش منابع آب به‌خصوص در کشور ایران، پیش‌بینی جریان رودخانه اهمیت زیادی یافته و لازم است از بهترین روش‌ها استفاده گردد. بدین منظور روش‌های خطی و غیرخطی زیادی وجود دارد. ازآنجایی‌که تشخیص خطی یا غیرخطی بودن دبی ماهانه دشوار است، در این پژوهش عملکرد برخی مدل‌های خطی و غیرخطی در پیش‌بینی جریان ماهانه‌ی رودخانه‌ی جامیشان واقع در استان کرمانشاه بررسی گردید. این مدل‌ها شامل مدل‌های خودهمبست...

Journal: :Neurocomputing 2003
Guoqiang Peter Zhang

Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with arti/cial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared with mixed conclusions in terms of the superiorit...

2016
Yan Jiang Guoqing Xinyan PENG Yongle LI

In order to improve the safety of train operation, a short-term wind speed forecasting method is proposed based on a linear recursive autoregressive integrated moving average (ARIMA) algorithm and a non-linear recursive generalized autoregressive conditionally heteroscedastic (GARCH) algorithm (ARIMA-GARCH). Firstly, the non-stationarity embedded in the original wind speed data is pre-processed...

Journal: :JCP 2012
Xiping Wang Ming Meng

Energy consumption time series consists of complex linear and non-linear patterns and are difficult to forecast. Neither autoregressive integrated moving average (ARIMA) nor artificial neural networks (ANNs) can be adequate in modeling and predicting energy consumption. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linea...

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