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...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

One of the most natural and primary ways of data collection in wireless sensor networks is to periodically report sensed data values from sensor node to aggregator. However, this kind of data collection mechanism comes at the cost of power consumption and packet collision. In this paper, we developed an automatic ARIMA (Auto Regressive Integrated Moving Average) modeling based data aggregation ...

Auto regressive integrated moving average (ARIMA) models have been widely used to calculate monthly time series data formed by interannual variations of monthly data or inter-monthly variation. However, the influence brought about by inter-monthly variations within each year is often ignored. An improved ARIMA model is developed in this study accounting for both the interannual and inter-monthl...

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis ...

Time series data mining (TSDM) techniques explores large amount of time series data in search of interesting relationships among variables. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis by adapting data mining concepts for analyzing time series data. The Feed Forward Neural Net is one of the most widely used neural ne...

Wide-spread real-time applications make it necessary for service providers to guarantee QoS parameters. This requires precise forecast of network traffic. A possible method of the forecast is measuring traffic then analyzing it and fitting model to the measured data, finally predicting the observed parameter using the fitted model. The efficiency of the prediction is decreased by outlying sampl...