Carbon dioxide can change the heat balance of atmosphere. To study relationship between CO 2 and temperature change, we use given concentrations to implement linear regression model, gray time series forecasting back-propagation auto-regressive moving average model establish growth function concentration. Errors are evaluated choose most suitable model. then in step one further predict future l...

Even in sustained vowels, durations of successive glottal cycles are not identical. They fluctuate quasi-randomly around an average. This phenomenon is known as jitter. More recently, correlation analysis has shown that perturbations of neighboring glottal cycles are interdependent, i.e. they are not purely random. We have shown that the non-random component of jitter can be modeled by means of...

Switchgrass is known as one of the best second-generation lignocellulosic biomasses for bioethanol production. Designing efficient switchgrass-based bioethanol supply chain (SBSC) is an essential requirement for commercializing the bioethanol production from switchgrass. This paper presents a mixed integer linear programming (MILP) model to design SBSC in which bioethanol demand is under auto-r...

Firms use time-series forecasting methods to predict sales. However, it is still a question which method forecaster best, if only single forecast needed. This study investigates and evaluates different sales methods: multiplicative Holt-Winters (HW), additive HW, seasonal auto regressive integrated moving average (SARIMA) [a variant of (ARIMA)], long short-term memory (LSTM) recurrent neural ne...

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...

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends machine learning (ML) technologies, automated recognition is becoming popular attracting many researchers. In this paper,...

The objective of the study was to describe seasonality of hospitalizations for heart failure in tropical climate as it has been described in cold climates. Seasonal Auto-regressive Integrated Moving-Average model was applied to time-series data of heart failure hospitalizations between 1996 and 2004 in Niteroi (Southeastern Brazil), collected from the Brazilian National Health Service Database....

Wide-spread real-time applications make it necessary for service providers to guarantee QoS parameters. This requires precise forecast of network traffic. A possible method of the forecast is measuring traffic then analyzing it and fitting model to the measured data, finally predicting the observed parameter using the fitted model. The efficiency of the prediction is decreased by outlying sampl...

energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. however, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. accordingly, in this paper a new strategy is proposed for electricity price forecast. the forecast strategy includes wavelet transform (wt...