Forecasting System Monitoring under Non-normal Input Noise Distributions

Sabeti, Hoda; Shebeeb, Omar Al

doi:10.4172/2169-0316.1000194

Cited by 1 publication

(1 citation statement)

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“…Different monitoring approaches have been proposed in the forecasting area. The tracking signal methods have been used to check the bias of forecasting methods (Sabeti et al, 2016) and also warn when there are unexpected outcomes from the forecast (Kumar & Murugan, 2017). Tracking signals can automatically detect changes in the forecast errors when the forecast is misbehaving.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear time-series prediction methodology based on neural networks and tracking signals

Bianchesi

Matta

Streitenberger

et al. 2022

Prod.

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Paper aims: This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series.Originality: This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient.Research method: Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings:The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice:This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

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Section: Introductionmentioning

confidence: 99%