The objective of the present study was to characterize the heart rate (HR) patterns of healthy males using the autoregressive integrated moving average (ARIMA) model over a power range assumed to correspond to the anaerobic threshold (AT) during discontinuous dynamic exercise tests (DDET). Nine young (22.3 +/- 1.57 years) and 9 middle-aged (MA) volunteers (43.2 +/- 3.53 years) performed three D...

This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of t...

This paper proposes a technique to implement wavelet analysis (WA) for improving a forecasting accuracy of the autoregressive integrated moving average model (ARIMA) in nonlinear time-series. With the assumption of the linear correlation, and conventional seasonality adjustment methods used in ARIMA (that is, differencing, X11, and X12), the model might fail to capture any nonlinear pattern. Ra...

This study examines the forecasting performance of Adaptive Neuro Fuzzy Inference System(ANFIS) compared in comparison to statistical autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) model in forecasting of rice yield production.. To assess the effectiveness of these models, we used 9 years of time series records for rice yield data in Malaysia from 1995 ...

Traffic prediction constitutes a hot research topic of network metrology. MultiStep ahead prediction allows to predict more values in the future. Then, the result can be used to act proactively in many prediction applications. In this work, the AutoRegressive Integrated Moving Average (ARIMA) model and the linear minimum mean square error (LMMSE) are used for multiStep predicting. Via experimen...

We propose diagnostics for ARIMA model fitting for time series formed by deleting observations from the data and measuring the change in the estimates of the parameters. The use of leave-one-out diagnostics is a well established tool in regression analysis. We demonstrate the efficacy of observation deletion based diagnostics for ARIMA models, addressing issues special to the time diagnostics b...

Accurate forecasting of demand under uncertain environment is one of the vital tasks for improving supply chain activities because order amplification or bullwhip effect (BWE) and net stock amplification (NSAmp) are directly related to the way the demand is forecasted. Improper demand forecasting results in increase in total supply chain cost including shortage cost and backorder cost. However,...

Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference sy...