This article attempts to present a basic method of time series analysis, modelling and forecasting performance of ARIMA, GARCH (1,1) and mixed ARIMA GARCH (1,1) models using historical daily close price downloaded through the yahoo finance website from the NASDAQ stock exchange for GE company (USA) during the period of 2001 to 2014. This paper also presents a brief analysis technique introducti...

Motor alteration is an important aspect of the elusive schizophrenia disorder, manifested both throughout the various phases of the disease and as a response to treatment. Tracking of patients’ movement, and especially in a closed ward hospital setting, can therefore shed light on the dynamics of the disease, and help alert staff to possible deterioration and adverse effects of medication. In t...

A renormalization group analysis is applied to autoregressive processes with an infinite series of coefficients. A simple fixed point is given by a random walk, and a second class is found that is proportional to the high order coefficients of fractional autoregressive integrated moving average (ARIMA) processes. The approach might be useful to detect nonstationarity in autoregressive processes.

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX). Data used to forecast is acquired from inventory database of...

Model identification is an important and complicated step within the autoregressive integrated moving average (ARIMA) methodology framework. This step is especially difficult for integrated series. In this article first investigate Box-Jenkins methodology and its faults in detecting model, and hence have discussed the problem of outliers in time series. By using this optimization method, we wil...

  In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...