Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model

By Chaoqing Yuan, Sifeng Liu, Zhigeng Fang. 2016

370 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/efaba64bd2d1a99b586a09f929dcd739ace15608

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yifeng Wang
2. Bufang Zhang

Citations in the corpus

• A Review of Deep Learning Models for Time Series Prediction (2019, neutral): “The problems are named as univariate [30] and multivariate [31], which are initially proposed for classifying ARIMA models [32], [33].”
• The Effect of Green Finance on Energy Sustainable Development: A Case Study in China (2019, background): “© 2019 Taylor & Francis Group, LLC energy demand and supply (Li and Zhang 2018; Wang, Li, and Li 2018; Yuan et al. 2016a).”