What question did this study set out to answer?

To review advancements in nonlinear and non-stationary time series forecasting and evaluate their capabilities across real-world complex applications.

January 13, 2015IIE Transactions

Time series forecasting for nonlinear and non-stationary processes: a review and comparative study

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To review advancements in nonlinear and non-stationary time series forecasting and evaluate their capabilities across real-world complex applications.
Reviewed predictive methodologies developed for time series exhibiting nonlinear dynamics, chaos, and non-stationarity.
Compared performance metrics across operational datasets from manufacturing environments and health informatics systems.
Conventional linear and stationary time series models fail to reliably capture state transitions and chaotic behaviors in transient conditions.
Standard methods impose high computational burdens and display strong sensitivity to the quantity and quality of prior information.

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Implication

Comparative review reveals limitations of standard time series forecasting in complex systems, highlighting the need for advanced nonlinear and non-stationary modeling approaches.

Cite This Study

Cheng et al. (2015) studied this question.

synapsesocial.com/papers/69d971afa1d151c65f684172 https://doi.org/10.1080/0740817x.2014.999180

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Implication

Comparative review reveals limitations of standard time series forecasting in complex systems, highlighting the need for advanced nonlinear and non-stationary modeling approaches.

Time series forecasting for nonlinear and non-stationary processes: a review and comparative study

Key Points

Discussion

Implication

Cite This Study

Time series forecasting for nonlinear and non-stationary processes: a review and comparative study

Key Points

Discussion

Implication

Cite This Study

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