This is a DEMO. This Time-series Prediction Benchmark is under the leadership of Professor Lei CHEN. Great thanks to TSlib.

🎉 NEWS:

• 2023-10-27 Support the Short-term-forcasting task.
• 2023-10-16 Add several benchmark datasets.
• 2023-10-09 Support ETSformer.
• 2023-09-30 Support Autoformer.
• 2023-09-23 Support Pyraformer.
• 2023-09-22 Support Informer.
• 2023-09-15 Support Reformer.
• 2023-09-02 Support Transformer.
• 2023-08-28 Support the Long-term-forcasting task.
• 2023-08-27 Finished Dataloader

• Installation
• Data
• How to run

.
├── ckpts            # Models will be saved here during training.
├── data             # Benchmark dataset.
├── dataloader       # Dataloader.
├── exp              # Long-term and short-term forecasting tasks
├── layer            # These codes are modified from mindspore.nn.layer, including the internal implementations of the model.
├── model     		 # Framework of the models.
├── results          # Results.
├── utils            # Other functions.
├── EDSR             # The implementation of Effective Data Selection and Replay for Unsupervised Continual Learning (EDSR).
├── exp_.py          # Main function 
└── README.md

For now, we only support CPU version MindSpore. We are executing the code using Python 3.8 on a Windows x64 platform. You can refer to here. run:

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.1.1/MindSpore/cpu/x86_64/mindspore-2.1.1-cp38-cp38-win_amd64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple

Please download the data from the Google Drive.

python exp.py  \
    --model 'Transformer' \
    --train_epoch 10

Other parameters

• model: Could choose from ['Transformer', 'Informer', 'Reformer', 'Pyraformer', 'Autoformer', 'ETSformer'].
• patience: For early stop.
• batch_size: Batch_size.
• learning_rate: Learning rate of the optimizer.
• features: Forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate.
• enc_in: Encoder input size.
• dec_in: Decoder input size.
• c_out: Output size.
• dropout: Dropout rate.
• embed: time features encoding, options:[timeF, fixed, learned].
• task_name: task name, options:[short_term_forecast, short_term_forecast].
• label_len: start token length.
• pred_len: prediction sequence length.

For long-term forecasting tasks:

For short-term forecasting tasks, we support four models: Transformer, Autoformer, Pyraformer, and Informer. Please configure args.features as 'S'.

• For Reformer, there is no CPU-based Mindspore equivalent of the PyTorch torch.einsum() function. Consequently, we continue to utilize the PyTorch version of this function in our code for its superior performance.(layers/reformer_attn.py) If you prefer not to use PyTorch, we also offer our own custom time-inefficient function, which can be found in the commented-out code at the same location.
• For Autoformer, ops.roll does not support CPU, and therefore we use the numpy instead.(layers/autoformer_attn.py)
• For ETSformer,
  • since the gradient is not supported for complex type multiplication currently, we have to do multiplication with nd.array format.(layers/autoformer_attn.py, layers/etsformer_attn.py)
  • since the mindspore.ops.FFTWithSize is not same as the torch.rfft/irfft, we use numpy instead.(layers/etsformer_attn.py)
• For now, we only provide long-term-forcast-task. We will support short-term-forcast-term in the future.