CEEMDAN-VMD-GRU

CEEMDAN-VMD-GRU Forecasting model (a light version of CEEMDAN_LSTM) is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM. It is a relatively imperfect module but beginners can quickly use it to make a decomposition-integration prediction by CEEMDAN, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (Torres et al. 2011), and LSTM, Long Short-Term Memory recurrent neural network (Hochreiter and Schmidhuber, 1997). If you use or refer to the content of this project, please cite paper: (F. Zhou, Z. Huang, C. Zhang, Carbon price forecasting based on CEEMDAN and LSTM, Applied Energy, 2022, Volume 311, 118601, ISSN 0306-2619.)

Pay attention to the look-ahead bias in this program.

Start to predict

0. Load raw data

Change CODE and PATH to load dataset from a csv file to create pd.Series as raw data.

df_raw_data = pd.read_csv(PATH+CODE+'.csv', header=0, parse_dates=['date'], date_parser=lambda x: datetime.datetime.strptime(x, '%Y%m%d'))

1. Just follow the steps without modification

Run the .py file without any modification, but please pay attention to pip install modules

if __name__ == '__main__': ...

2. Plot and Output

All functions' return are pd.DataFrame.

Use df.plot() to show the figure.

df_name.plot(figsize=(12,6), title='CEEMDAN-VMD-GRU Predicting Result')

Use pd.DataFrame.to_csv() to download the result.

pd.DataFrame.to_csv(df_name, PATH+'CEEMDAN_VMD_GRU_predict_output.csv')

Postscript

For more functions, please use CEEMDAN_LSTM. Thanks!
If you have any questions, please leave your comment or email me.