3 We examine the effect of damping X-12-ARIMA's estimated seasonal variation on the accuracy of its seasonal adjustments of time series. Two methods for damping seasonals are proposed. In a simulation experiment, we generated time series data for each of 90 distinct experimental conditions that, in aggregate, characterize the variety of monthly series in the M3-competition. X-12-ARIMA consisten...

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...

Time series forecasting plays a significant role in numerous applications, including but not limited to, industrial planning, water consumption, medical domains, exchange rates and consumer price index. The main problem is insufficient accuracy. present study proposes hybrid methods to address this need. proposed method includes three models. first model based on the autoregressive integrated m...

b a c k g r o u n d & aim: one of the common used models in time series is auto regressive integrated moving  average  (arima)  model.  arima  will  do  modeling  only  linearly.  artificial  neural networks (ann) are modern methods that be used for time series forecasting.  these models can identify non-linear relationships  among data. the breast cancer has the most mortality of cancers among...

Filters used to estimate unobserved components in time series are often designed on a priori grounds, so as to capture the frequencies associated with the component. A limitation of these filters is that they may yield spurious results. The danger can be avoided if the so-called ARIMA-model-based (AMB) procedure is used to derive the filter. However, parsimony of ARIMA models typically implies ...

Climate and rainfall are highly non-linear and complicated phenomena, which require sophisticated computer modelling and simulation for accurate prediction. An artificial intelligence technology allows knowledge processing and can be used .as forecasting tool. For example, the application of Artificial Neural Networks (ANN), to predict the behaviors of nonlinear systems has become an attractive...

BACKGROUND Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China, where human cases account for 90 % of the total global cases. Yiyuan County is one of the most serious affected areas in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence in Yiyuan to make the control of HFRS more effective. METHODS The study was based on the repor...

OBJECTIVES The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. METHODS From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy ...

The import and export of goods normally require the involvement of many different sectors from purchasing, manufacturing, transporting, inventory, distribution, etc. In order to have proper plans, accurately forecast the volume of imported-exported goods is the core issue. By comparing the performance of autoregressive integrated moving average (ARIMA) model, Grey model, and their joint Fourier...

Worldwide, influenza is estimated to result in approximately 3 to 5 million annual cases of severe illness and approximately 250,000 to 500,000 deaths. We need an accurate time-series model to predict the number of influenza patients. Although time-series models with different time lags as feature spaces could lead to varied accuracy, past studies simply adopted a time lag in their models witho...