Abstract
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.
Keywords:
Nonlinear time series; Time series forecasting; Neural networks; Tracking signals; Design of Experiments