Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mini...

ARIMA is a popular method to analyze stationary univariate time series data. There are usually three main stages to build an ARIMA model, including model identification, model estimation and model checking, of which model identification is the most crucial stage in building ARIMA models. However there is no method suitable for both ARIMA and SARIMA that can overcome the problem of local optima....

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-insample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, 1step-ahead and multi-step-ahead forecasting are compared for eac...

This research applied the model that simulates the effects of inflow water quality, treatment flow rate and outflow water quality on drinking water treatment plant. The model is not a physical chemistry model. However it can evaluate the performance of sedimentation process as a statistical model. The model used transfer function ARIMA model for the prediction of turbidity on sedimentation rese...

Despite their theoretical limitations, ARIMA models are widely used in real-life forecasting tasks. Parzen has proposed an extension of ARIMA models: ARARMA models. ARARMA models consist of an AR model followed by an ARMA model. Following Parzen approach,-NARMA neural network are MLP, the units of which are simple non-linear ARMA-based models (-NARMA units). They are a non-linear extension of A...

The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine concentration time series data. This paper proposes an ARIMA-LSTM combined forecasting based on autoregressive integrated moving average (ARIMA) long short-term memory (LSTM) recurrent neural network. In model, ARIMA used to process historical data obtain corresponding linear...

the proof of the local nilpotence theorem for 4-engel groups was completed by g. havas and m. vaughan-lee in 2005. the complete proof on the other hand is spread over several articles and the aim of this paper is to give a complete coherent linear version. in the process we are also able to make few simplifications and in particular we are able to merge two of the key steps into one.

One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...

AIM To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon. METHODS The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis ...