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

By Changqing Cheng, Akkarapol Sa-ngasoongsong, O. Beyca, Trung-Quôc Le, Hui Yang, Zhenyu James Kong, Satish T. S. Bukkapatnam. 2015

255 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1517be4cf3ee3cb1fcfbefa8631ec2381e5c9a42

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In Signal Processing

1. Soundar Kumara *

Citations in the corpus

• A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks (2023, unknown): “According to the work of Cheng et al. [12], conventional forecasting approaches, such as ARIMA techniques, cannot adequately capture the evolution of these systems from the perspectives of forecasting accuracy, computational workload, and sensitivity to the quantity and quality of a priori input…”
• The internet of things for smart manufacturing: A review (2019, background): “The manufacturing workshop environment also has a high level of nonstationarity, uncertainty and noise (Cheng et al , 2015).”