background road traffic accidents and their related deaths have become a major concern, particularly in developing countries. iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. in this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying arima models on data...

kahman is the most beneficial river in alashtar city for agriculture and aquiculture. as hydrology processes have random nature, statistics and probability are base of analysis of these processes and time series are used for this purpose. the first step in time series analysis includes parameters variation through time. second step is to stationary data, third is normalization and forth is mode...

Abstract: In 2007, in Kazakhstan, there was a transition of TDM (Time Division Multiplexing) circuit-switched technologies to IP (Internet Protocol) packet technology, which created modern infrastructure for the ICT (information communication technologies) sphere. The advent IoT Things) concept has led growth functioning network at faster rate. It is currently developing direction cognitive inf...

In this article, a special type of orthogonalization is obtained to construct a multiple input transfer function model. By using this technique, construction of a transfer function model is divided to sequential construction of transfer function models with less input time series. Furthermore, based on real and simulated time series we provide an instruction to adequately perform the stages of ...

in this work, the time series modeling was used to predict the tazareh coal mine risks. for this purpose, initially, a monthly analysis of the risk constituents including frequency index and incidence severity index was performed. next, a monthly time series diagram related to each one of these indices was for a nine year period of time from 2005 to 2013. after extrusion of the trend, seasonali...

Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...