In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surf...

In this work, the seasonality and performance loss rates of eleven grid-connected photovoltaic (PV) systems of different technologies were evaluated through seasonal adjustment. The classical seasonal decomposition (CSD) and X-12-ARIMA statistical techniques were applied on monthly DC performance ratio, RP, time series, constructed from field measurements over the systems' first five years of o...

In an e€ort to forecast daily maximum ozone concentrations, many researchers have developed daily ozone forecasting models. However, this continuing worldwide environmental problem suggests the need for more accurate models. Development of these models is dicult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, a neural net...

This paper presents a predictive study applied to a manufacturing equipment in order to predict malfunctions, and consequently enabling predictive maintenance practices. ARIMA forecasting methods are successfully compared with neural networks models, both used over data obtained from a monitoring system that continuously keeps track of the relevant equipment parameters. The results show that bo...

Many environmental and socioeconomic time–series data can be adequately modeled using Auto-Regressive Integrated Moving Average (ARIMA) models. We call such time–series ARIMA time–series. We consider the problem of clustering ARIMA time–series. We propose the use of the Linear Predictive Coding (LPC) cepstrum of time–series for clustering ARIMA time–series, by using the Euclidean distance betwe...

In statistics, signal processing, and mathematical finance; a time series is a sequence of data points that measured at uniform time intervals. The prediction of time series is a very complicated process. In this paper, an improved Adaptive Neuro Fuzzy Inference System (ANFIS) is taken for predicting Mackey-Glass which is one of the chaotic time series. In the modeling of linear and stationary ...