This study compares two new seasonal adjustment methods designed to handle outliers and structural changes: X-IZARIMA and GAUSUM-STM. X12-ARIMA is a successor to the X-ll-ARIMA seasonal adjustment method, and is being developed at the U.S. Bureau of the Census (Findley et al. (1988)). GAUSUM-STM is a non-Gaussian method using time series structural models, and was developed for this study based...

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In th...

A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract fr...

و arima-garch ،garch ،arima هدف اصلی در مقاله حاضر مقایسه دقت پیش بینی چهار مدلدر تخمین و پیش بینی شاخص قیمت بورس اوراق بهادار تهران )تپیکس( است. برای این منظور، state spaceداده های روزانه 1 بهمن سال 1389 تا 30 بهمن سال 1392 به عنوان درون داده و 1 اسفند 1392 تا 30 اردیبهشت1393 به عنوان برون داده، استفاده شده اند. از طرفی دیگر، برای بررسی بیشتر و افزایش دقت پیش بینی مدل هایمذکور برای شاخص تپیکس ...

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...

Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This paper introduces an ARIMA based model for the real time prediction of VBR video traffic. The methodology presented here can successfully addresses the challenges in traffic prediction such as accuracy in prediction, resource management and utilization. ARIMA application on ...

Statistical evidence suggests that the autocorrelation function of a compressed-video sequence is better captured by (k) = e ? p k than by (k) = k ? = e ? log k (long-range dependence) or (k) = e ?k (Markovian). A video model with such a correlation structure is introduced based on the so-called M=G=1 input processes. Though not Markovian, the model exhibits short-range dependence. Using the qu...