An open source univariate time series forecasting framework that provides following features:
- A general framework intergrated with data preprocess, hyper-parameters setting, hyper-parameters tuning, model training, model evaluation, and experiment logging.
- An easy user-replaced model coding paradigm compatible with both statistical, stochastic, and training models.
- Ready-to-use forecasting models, supported with both GPU acceleration or CPU only.
- python >= 3.6
- pytorch = 1.9.1
- CUDA (as required as pytorch, if using GPU)
- ray = 1.6.0 (as requried by the specific optimizaiton algorithm, if using TaskTuner)
- scikit-learn = 1.0.2
- Strong deep neural networks.
- Classic statistical and machine learning models.
- Promising neural networks with random weights.
- Our proposed models.
The training models we implemented are referred to these papers.
The stochastic models we implemented are referred to these papers.
Our proposed models are corresponding to these papers.
Model | Paper |
---|---|
MSVR | Multi-step-ahead time series prediction using multiple-output support vector regression |
ESM-CNN | Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks |
ETO-SDNN | Growing stochastic deep neural network for time series forecasting with error-feedback triple-phase optimization |
- This framework is created by Xinze Zhang, Qi Sima, and Siyue Yang, supervised by Prof. Yukun Bao, in the school of Management, Huazhong university of Science and Technology (HUST).
Notice
- The DeepAR provided in this repository is modified based on the work of TimeSeries. Yunkai Zhang, Qiao Jianga, and Xueying Ma are original authors of TimeSeries.
- The ConvRNN provided in this repository is modified based on the work of ConvRNN. KurochkinAlexey, Fess13 are original authors of ConvRNN.
- The PSO-GESN provided in this repository is modified based on the source code created by Qi Sima.