Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?

The repo is the official implementation for the paper: Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?.

Introduction

Transformer-based application on Long-term Time series Forecasting (LTSF) tasks still has two major issues that need to be further investigated: 1) Whether the sparse attention mechanism designed by these methods actually reduce the running time on real devices; 2) Whether these models need extra long input sequences to guarantee their performance? The answers given in this paper are negative. Therefore, to better copy with these two issues, we design a lightweight Period-Attention mechanism (MABOformer). Furthermore, to take full advantage of GPUs for fast hyperparameter optimization (e.g., finding the suitable input length), a Multi-GPU Asynchronous parallel algorithm based on Bayesian Optimization (MABO) is presented. Compared with the state-of-the-art methods, the prediction error of MABOformer reduced by 13% and 26% for multivariate and univariate forecasting, respectively. In addition, MABO reduces the average search time by 46% while finding better hyperparameters. As a conclusion, this paper indicates that LTSF may not need complex attention and extra long input sequences.

Contributions

• It is found that although the computational complexity of those traditional Transformer-based LTSF methods is theoretically reduced, their running time on practical devices remains unchanged. Meanwhile, it is found that both the input length of the series and the kernel size of the MA have impacts on the final forecast.
• A novel Period-Attention mechanism (MABOformer) is proposed, which renovates the aggregation of long-term subseries via explicit periodicity and short-term subseries via built-in proximity. In addition, a gate mechanism is built into Period-Attention to adjust the influence of the attention score to its output, which guarantees higher prediction performance and shorter running time on real devices.
• A multi-GPU asynchronous parallel search algorithm based on Bayesian optimization (MABO) is presented. MABO allocates a process to each GPU via a queue mechanism, and then creates multiple trials at a time for asynchronous parallel search, which greatly accelerates the search speed.
• MABOformer reduces the prediction error of state-of-the-art (SOTA) methods by around 14.8% and 22.6% for multivariate and univariate forecasting, respectively. Besides, MABO reduces the average search time by around 46% while finding out better hyperparameters.

Training MABOformer with MABO

Clone the code repository

git clone git@github.com:Anoise/MABOformer.git

Training on ETTm2 Dataset

Go to the directory "MABOformer/", and run

python eval_async_mGPUs_PF.py
--num_gpus 8
--num_trails 32
--model MABOformer
--data ETTm2
--root_path ../data/ETT-small/
--data_path ETTm2.csv
--pred_len 192
--enc_in 7
--enc_in 7
--c_out 7

Training on Electricity Dataset

Go to the directory "MABOformer/", and run

python eval_async_mGPUs_PF.py
--num_gpus 8
--num_trails 32
--model MABOformer
--data custom
--root_path ../data/electricity/
--data_path electricity.csv
--pred_len 192
--enc_in 321
--dec_in 321
--c_out 321

Training on Exchange Dataset

Go to the directory "MABOformer/", and run

python eval_async_mGPUs_PF_v2.py 
--num_gpus 8 
--num_trails 32 
--model MABOformer 
--data custom 
--root_path ../data/exchange_rate/ 
--data_path exchange_rate.csv  
--pred_len 192 
--enc_in 8 
--dec_in 8 
--c_out 8

Training on Traffic Dataset

Go to the directory "MABOformer/", and run

python eval_async_mGPUs_PF_v2.py
--num_gpus 8 
--num_trails 32 
--model MABOformer 
--data custom 
--root_path ../data/traffic/ 
--data_path traffic.csv  
--pred_len 192 
--enc_in 862 
--dec_in 862 
--c_out 862

Training on Weather Dataset

Go to the directory "MABOformer/", and run

python eval_async_mGPUs_PF_v2.py 
--num_gpus 8 
--num_trails 32 
--model MABOformer 
--data custom 
--root_path ../data/weather/ 
--data_path weather.csv  
--pred_len 192 
--enc_in 21 
--dec_in 21 
--c_out 21

Training on ILI Dataset

Go to the directory "MABOformer/", and run

python eval_async_mGPUs_PF_v2.py 
--num_gpus 8 
--num_trails 32 
--model MABOformer 
--data custom 
--root_path ../data/illness/ 
--data_path national_illness.csv  
--pred_len 36 
--enc_in 7 
--dec_in 7 
--c_out 7

Testing, taking ETTm2 dataset as an example,

python run.py
--is_training 0
--model MABOformer
--data ETTm2
--root_path ../data/ETT-small/
--data_path ETTm2.csv
--pred_len 192
--enc_in 7
--enc_in 7
--c_out 7

Note that:

• Model was trained with Python 3.7 with CUDA 10.X.
• Model should work as expected with pytorch >= 1.7 support was recently included.

Performace on Multivariate setting

Performace on Univariate setting

Effectiveness of MABO

Citations

Daojun Liang, Haixia Zhang, Dongfeng Yuan, Xiaoyan Ma, Dongyang Li and Minggao Zhang, Does Long-Term Serie Forecasting Need Complex Attention and Extra Long Inputs? arXiv preprint arXiv: 2306.05035 (2023).

@article{liang2023does,
  title={Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?},
  author={Liang, Daojun and Zhang, Haixia and Yuan, Dongfeng and Ma, Xiaoyan and Li, Dongyang and Zhang, Minggao},
  journal={arXiv preprint arXiv:2306.05035},
  year={2023}
}