Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average

14 references·10 citing·50 second hop

FromToDepth

this work references cited by second hop

References

1971Time Series Analysis, Forecasting and Control.2,771 cited
2010A novel hybridization of artificial neural networks and ARIMA models for time series forecasting825 cited
2014A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data505 cited
2018Forecasting stock market crisis events using deep and statistical machine learning techniques303 cited
2007Online Learning: Theory, Algorithms, and Applications297 cited
2014The effects of oil price shocks on output and inflation in China216 cited
2018Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach163 cited
2009Forecasting energy consumption in Taiwan using hybrid nonlinear models145 cited
2018Carbon price forecasting with variational mode decomposition and optimal combined model142 cited
2019Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility101 cited
2019A novel hybrid multivariate nonlinear grey model for forecasting the traffic-related emissions100 cited
2013Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm86 cited
2017Grey-Markov prediction model based on background value optimization and central-point triangular whitenization weight function83 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

2018

The VEC-NAR model for short-term forecasting of oil prices

78 cited

Cited by

2021Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach74 cited
2024Machine Learning-Based Scrap Steel Price Forecasting for the Northeast Chinese Market71 cited
2023Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level68 cited
2021Forecasting the seasonal natural gas consumption in the US using a gray model with dummy variables55 cited
2023Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods48 cited
2025Predicting Scrap Steel Prices Through Machine Learning for South China42 cited
2025Machine learning predictions of China commodity price indices41 cited
2021Coal consumption forecasting using an optimized grey model: The case of the world's top three coal consumers41 cited
2024Steel price index forecasts through machine learning for northwest China39 cited
2025Price predictions of scrap steel for north China via machine learning39 cited