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...

Dataset with Outliers causes poor accuracy in future analysis of data mining tasks. To improve the performance of mining task, it is necessary to detect and revamp of outliers which are there in the dataset. Existing techniques like ARMA (Auto-Regressive Moving Average), ARIMA (AutoRegressive Integrated Moving Average) and Multivariate Linear Gaussian state space model don't consider the p...

This paper assesses the predictability of network traffic by considering two metrics: 1) how far into the future a traffic rate process can be predicted for a given error constraint; 2) what the minimum prediction error is over a specified prediction time interval. The assessment is based on two stationary traffic models: the Auto-Regressive Moving Average (ARMA) model and the Markov-Modulated ...

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average w...

The difference between training and testing environments is the major reason of performance degradation of speech recognition. In this paper, to further decrease the mismatch, we apply temporal filtering, Auto-Regression and Moving-Average (ARMA) filtering or RelAtive SpecTrAl (RASTA) filtering, as a post-processor for the log-Energy dynamic Range Normalization-Cepstral Mean and Variance Normal...

Both the fractional Brownian motion (fBm) and the Auto-regressive Integrated Moving Average (ARIMA) models have been applied to teletraffic scenarios in recent years. These models became popular after the discovery that Ethernet and VBR video data appear to possess the property of selfsimilarity. However the results presented in this paper suggest that Ethernet data is more impulsive than traff...

Abstract Auto-regressive integrated moving average (ARIMA) assessment of traffic noise was conducted on different routes in Port Harcourt, Nigerian metropolis. This achieved by measuring the various asphalt flexible and concrete rigid pavement structures with a meter for sound measurement regards to volume traffic, vehicle movement rate, location away from midpoint highway. The peak obtained at...

Unit commitment (UC) stands out as a significant challenge in electrical power systems. With the rapid growth demand and pressing issues of fossil fuel scarcity global warming, it has become crucial to enhance utilization renewable energy sources. This study focuses on addressing UC problem by incorporating wind farm proposes modified version metaheuristic African vultures optimization algorith...