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...

Predicting daily occupancy is extremely important for the revenue management of individual hotels. However, daily occupancy can fluctuate widely and is difficult to forecast accurately based on existing forecasting methods. In this paper, Ensemble Empirical Mode Decomposition (EEMD)—a novel method—is introduced, and an individual hotel is chosen to test the effectiveness of EEMD in combination ...

This article explores an alternative state space representation for ARIMA models to that usually advocated. The alternative representation has minimal state order. More importantly, it has more convenient Kalman filter convergence properties. This convergence reveals the concrete connection between classical infinite sample representations based on lag polynomials and the recursive Kalman filte...

Consider the fractional ARIMA time series with innovations that have innnite variance. This is a nite parameter model which exhibits both long-range dependence (long memory) and high variability. We prove the consistency of an estimator of the unknown parameters which is based on the periodogram and derive its asymptotic distribution. This shows that the results of Mikosch, Gadrich, Kl uppelber...

This study gives an inside view of the application of ARIMA time series model to forecast the future Gold price in Indian browser based on past data from November 2003 to January 2014 to mitigate the risk in purchases of gold. Hence, to give guideline for the investor when to buy or sell the yellow metal. This financial instrument has gained a lot of momentum in recent past as Indian economy is...

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...

Flooding is the most common natural disaster and continues to increase in frequency intensity due climate changes [7]. Currently, there a lack of efficient tools predict flooding. This research aimed create Time Series Machine Learning (ML) program using Auto Regressive Moving Average (ARIMA) models forecast streamflow, one prominent factors flood prediction. A streamflow dataset from Ganges Ri...

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...

in this study, the situation of iran, u.s and turkey's pistachio export is investigated. to this purpose, revealed comparative advantage (rca) index is calculated based on agricultural and total economy export, separately, then forecasted by using auto- regressive integrated moving average (arima) approached, for 2008-2013. the results show that considering both commodity baskets, turkey a...