OpenForecasting

An open source 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.
• As for now, we only support univariable time series forecasting. In the future, the multivariable time serires forecasting will be officially provided.

The experiments need to be configured by the python files in the folder exp. To replicate or run the experiment in the exp folder, e.g., exp/encoder/demo.py, just execute:

python exp/encoder/demo.py

or:

python main.py -cuda -test -datafolder exp/encoder -dataset demo -exp_name RL -H 2 -model rnn -rep_times 1

Main Dependence

To install the dependence of the running environment, using the following commands:

cd _requirement
conda create --name opents --file packages.txt
conda activate opents
conda install pip
pip install -r requirements.txt

For the follower in China, we suggest to config the mirror for conda and pip.

Conda mirror

Create the .condarc file if it does not exist.

touch ~/.condarc

Then copy the following mirrors to the .condarc:

channels:

  - http://mirrors.bfsu.edu.cn/anaconda/pkgs/main

  - http://mirrors.bfsu.edu.cn/anaconda/pkgs/free

  - http://mirrors.bfsu.edu.cn/anaconda/pkgs/r

  - http://mirrors.bfsu.edu.cn/anaconda/pkgs/pro

  - http://mirrors.bfsu.edu.cn/anaconda/pkgs/msys2

show_channel_urls: true

custom_channels:

  conda-forge: http://mirrors.bfsu.edu.cn/anaconda/cloud

  msys2: http://mirrors.bfsu.edu.cn/anaconda/cloud

  bioconda: http://mirrors.bfsu.edu.cn/anaconda/cloud

  menpo: http://mirrors.bfsu.edu.cn/anaconda/cloud

  pytorch: http://mirrors.bfsu.edu.cn/anaconda/cloud

  simpleitk: http://mirrors.bfsu.edu.cn/anaconda/cloud

  intel: http://mirrors.bfsu.edu.cn/anaconda/cloud

Then clean the cache and test it:

conda update --strict-channel-priority --all  
conda clean 

Pip mirror

With tencent cloud, create the pip configuration file by mkdir ~/.pip; nano ~/.pip/pip.conf, and paste the following:

[global]

index-url = https://mirrors.cloud.tencent.com/pypi/simple/


[install]

trusted-host=mirrors.cloud.tencent.com


timeout = 120

Save the file pip.conf.

Provided models

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• 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.

Model	Paper
DeepAR	DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
ConvRNN	Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time
RNN (Elman, GRU, LSTM)	Recurrent neural networks for time series forecasting: current status and future directions
CNN	Convolutional neural networks for energy time series forecasting
MLP	PSO-MISMO modeling strategy for MultiStep-ahead time series prediction

The stochastic models we implemented are referred to these papers.

Model	Paper
RVFL	A review on neural networks with random weights
IELM	Extreme learning machine: theory and applications
SCN	Stochastic configuration networks: fundamentals and algorithms
ESN	Optimization and applications of echo state networks with leaky-integrator neurons
GESN	Growing echo-state network with multiple subreservoirs
DESN	Design of deep echo state networks
PSO-GESN	PSO-based growing echo state network

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

Acknowledgement

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• This framework is created by Xinze Zhang, 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.