In this paper, we investigate the robustness of Feed Forward Neural Network (FFNN) ensemble models applied to quarterly time series forecasting tasks, by comparing their prediction ability with that of Seasonal Auto-regressive Integrated Moving Average (SARIMA) models. We obtained adequate SARIMA models which required statistical knowledge and considerable effort. On the other hand, FFNN ensemb...

This paper introduces the “Energy-Environmental Efficiency” concept of building a low-carbon dispatch model of wind-incorporated power systems from the perspective of environmental protection and low-carbon dispatch promotion based on the existing economic environmental dispatch. A rolling auto-regressive and moving-average model is adopted to forecast wind speeds for the next 24 h and reduce t...

An approach to handling colored observation noise in large least-squares (LS) problems is presented. The handling of colored noise is reduced to the problem of solving a Toeplitz system of linear equations. The colored noise is represented as an auto regressive moving-average (ARMA) process. Stability and invertability of the ARMA model allows the solution of the Toeplitz system to be reduced t...

Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of l...

Horizontal displacement of Hoa Binh dam in operation phase is analyzed and then forecasted by using three methods: the multi-regression model (MTR), Seasonal Integrated Auto-regressive Moving Average (SARIMA) Back-propagation Neural Network (BPNN) model. The monitoring data Dam 137 periods, including horizontal displacement, time, reservoir water level air temperature, are used for experiments....

This paper proposes a reliability estimation method based on Step-Stress Accelerated Degradation Testing (SSADT) data analysis using unequal interval time series analysis. A MultiRegression Time Varying Auto-Regressive (MRTVAR) degradation time series model is proposed. Product SSADT data are treated as unequal interval composite time series and described using MRTVAR time series model and util...

in this study, the situation of iran, u.s and turkey's pistachio export is investigated. to this purpose, revealed comparative advantage (rca) index is calculated based on agricultural and total economy export, separately, then forecasted by using auto- regressive integrated moving average (arima) approached, for 2008-2013. the results show that considering both commodity baskets, turkey and ir...

Wireless health monitoring schemes are innovative techniques, which effectively remove the disadvantages associated with current wire-based sensing systems, i.e., high installation and upkeep costs. However, recorded data sets may have relative time-delays due to the blockage of sensors or inherent internal clock errors. In this paper, two algorithms are proposed for the synchronization of the ...

In this study, it was investigated to what extent linear auto regressive models with external input (ARX) and auto regressive moving average models with external input (ARMAX) could be used to describe the inside air temperature of an unheated, naturally ventilated greenhouse under Western European conditions. Outside air temperature and relative humidity, global solar radiation, and cloudiness...

A number of empirical and analytical studies demonstrated that the ultrasound RF echo reflected from tissue exhibits 1/f characteristics. In this paper, we propose to model 1/f characteristics of the ultrasonic RF echo by a novel parsimonious model, namely the fractional differencing auto regressive moving average (FARMA) process, and evaluated diagnostic value of model parameters for breast ca...