[Paper]     [Project page]

This is the official PyTorch implementation of Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift.

Statistical properties such as mean and variance often change over time in time series, i.e., time-series data suffer from a distribution shift problem. This change in temporal distribution is one of the main challenges that prevent accurate time-series forecasting. To address this issue, we propose a simple yet effective normalization method called reversible instance normalization (RevIN), a generally-applicable normalization-and-denormalization method with learnable affine transformation. The proposed method is symmetrically structured to remove and restore the statistical information of a time-series instance, leading to significant performance improvements in time-series forecasting, as shown in Fig. 1. We demonstrate the effectiveness of RevIN via extensive quantitative and qualitative analyses on various real-world datasets, addressing the distribution shift problem.

The code was developed using python 3.8 on Ubuntu 18.04.

The experiments were performed on a single NVIDIA TITAN RTX or NVIDIA TITAN Xp.

1. Install PyTorch >= v.1.8.0 following the official instruction.
   • tested with PyTorch v.1.8.0 and PyTorch v.1.11.0.
2. Clone this repository:

   git clone https://github.com/ts-kim/RevIN.git
   

RevIN calculates the mean and standard deviation of each feature separately for each sequence in a mini-batch.

To be reversible, the input and output tensors should have the same number of features. The input tensors should be provided as (..., feature). For example,

• x_in: (batch_size, sequence_length, num_features)
• x_out: (batch_size, prediction_length, num_features)

RevIN can be added in any arbitrarily chosen layers of a model as follows:

>>> from RevIN import RevIN
>>> revin_layer = RevIN(num_features)
>>> x_in = revin_layer(x_in, 'norm')
>>> x_out = blocks(x_in) # your model or subnetwork within the model
>>> x_out = revin_layer(x_out, 'denorm')

We updated the training and evaluation codes for the baselines, Informer and SCINet.

Please go to the baselines forder or the link below.

• Informer
• SCINet

If you find this work or code is helpful in your research, please cite:

@inproceedings{kim2021reversible,
  title     = {Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift},
  author    = {Kim, Taesung and 
               Kim, Jinhee and 
               Tae, Yunwon and 
               Park, Cheonbok and 
               Choi, Jang-Ho and 
               Choo, Jaegul},
  booktitle = {International Conference on Learning Representations},
  year      = {2021},
  url       = {https://openreview.net/forum?id=cGDAkQo1C0p}
}