• ParallelTime (Parallel Time) selects the contributions of short-term, long-term, and global memory for each time series patch.
• We demonstrate that parallel Mamba-Attention architecture is currently the most effective approach for long-term time series forecasting.
• Our model, ParallelTime, achieves SOTA performance on real-world benchmarks, delivering better results from previews models with fewer parameters and lower computational cost, critical for real-time forecasting.

1. Obtain the fundamental long-term forecasting datasets, including Weather, Illness, Traffic, Electricity, and ETT (comprising 4 datasets) You can do it by downloading it from this Autoformer, and move them into dataset/ directory.

├── dataset
│   ├── electricity
│   │   └── electricity.csv
│   ├── ETT-small
│   │   ├── ETTh1.csv
│   │   ├── ETTh2.csv
│   │   ├── ETTm1.csv
│   │   └── ETTm2.csv
│   ├── illness
│   │   └── national_illness.csv
│   ├── traffic
│   │   └── traffic.csv
│   └── weather
│       └── weather.csv 

2. Install requirements. pip install uv followed by uv sync which will install all the project dependencies (dont forget to change the source to .venv).

3. Look through our scripts located at ./scripts which run 4 runs for diffrent prediction length depend on the dataset. You'll find the core of ParallelTime in models/ParallelTime.py. For example, to get the multivariate forecasting results for ETTh1 dataset, just run the following command sh ./scripts/etth1.sh

For the other datasets run on a Linux machine:

sh ./scripts/<script_name>.sh

After the run ends for each prediction length, you can open ./result_long_term_forecast_<dataset_name>.txt to view the results once the model run is complete. Additionally, logs and configurations are saved for each run in ./multirun/current_date/<dataset_name>/run_time, which can be used to review more detailed results.

We are deeply grateful for the valuable code and efforts contributed by the following GitHub repositories.

• https://github.com/thuml/iTransformer
• https://github.com/yuqinie98/PatchTST

If you have any questions or concerns, please contact us or submit an issue