AdaWaveNet

AdaWaveNet is a comprehensive framework for time series forecasting, imputation, and super-resolution tasks.

Please refer to the paper for more details.

@article{yu2024adawavenet,
  title={AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis},
  author={Yu, Han and Guo, Peikun and Sano, Akane},
  journal={arXiv preprint arXiv:2405.11124},
  year={2024}
}

Features

• Long-term and Short-term Forecasting: Supports models like Autoformer, Transformer, TimesNet, and more.
• Imputation: Handles missing data in time series.
• Super Resolution: Enhances the resolution of time series data.

Requirements

The project requires the following Python packages, which can be installed using the requirements.txt file:

Usage

The main entry point for running experiments is the run.py script. It supports various command-line arguments to configure the experiments. Here is an example of how to run a long-term forecasting task:

python -u run.py \
--task_name long_term_forecast \
--is_training 1 \
--root_path ./dataset/weather/ \
--data_path weather.csv \
--model_id weather_96_96 \
--model AdaWaveNet \
--data custom \
--features M \
--seq_len 96 \
--label_len 48 \
--pred_len 96 \
--e_layers 3 \
--d_layers 1 \
--factor 3 \
--enc_in 21 \
--dec_in 21 \
--c_out 21 \
--des 'Exp' \
--d_model 512 \
--d_ff 512 \
--itr 1 \
--lifting_levels 3 \
--lifting_kernel_size 7 \
--n_cluster 4 \
--learning_rate 0.0005 \
--batch_size 16

Configuration

The run.py script accepts various arguments to configure the experiment:

• --task_name: The name of the task (e.g., long_term_forecast, short_term_forecast, imputation, classification, anomaly_detection).
• --is_training: Whether to train the model (1 for training, 0 for testing).
• --model: The model to use (e.g., Autoformer, Transformer, TimesNet).
• --seq_len, --label_len, --pred_len: Sequence lengths for input, label, and prediction.
• --e_layers, --d_layers: Number of encoder and decoder layers.
• --learning_rate: Learning rate for the optimizer.
• --batch_size: Batch size for training.

For a full list of arguments, refer to the run.py script.

Acknowledgments

This project is based on the Time-Series-Library Repository https://github.com/thuml/Time-Series-Library and other state-of-the-art time series models.