This repository contains the implementation of the Decomposition State-Space Recurrent Neural Network (DSSRNN), a novel framework designed for accurate long-term and short-term forecasting in time-series analysis. DSSRNN innovatively integrates decomposition analysis with state-space models and physics-based equations, focusing on improving forecasting accuracy and computational efficiency, particularly in predicting indoor air quality levels.
Below is the structure of this repository, detailing the primary components and their purpose:
├── data_provider/ # Modules for data preprocessing and loading
├── dataset/ # Directory for dataset storage
├── DSSRNN-classification/ # Implementation of DSSRNN for classification tasks
├── DSSRNN-imputation/ # Implementation of DSSRNN for data imputation
├── exp/ # Experiment scripts and configuration files
├── FEDformer/
├── layers/ # Custom neural network layers used in models
├── models/ # Directory containing different model implementations
├── results/ # Output folder for results and model checkpoints
├── scripts/ # Utility scripts for various tasks
├── utils/ # Helper functions and utility modules
├── .gitignore # Specifies intentionally untracked files to ignore
├── get-pip.py # Script to install pip
├── ReadMe.md
└── requirements.txt # List of dependencies to install
Our script files for the experiment are located in ./scripts:
| Files | Interpretation |
|---|---|
| /co2.sh | A single experiment using DSSRNN on a single dataset |
| EXP-Embedding/co2_all2.sh | Experiments on all the datasets for the linear models |
| EXP-Embedding/co2_all.sh | experiments on all the dataset for the transformers |
The models and the experiments can be modified according to your preference.
This code is simply built on the code base of LTSF-Linear. We appreciate the following GitHub repos a lot for their valuable code bases:
The implementation of LTSF-Linear is from https://github.com/cure-lab/LTSF-Linear
The implementation of Autoformer, Informer, and Transformer is from https://github.com/thuml/Autoformer
The implementation of FEDformer is from https://github.com/MAZiqing/FEDformer
Please confirm that Conda has been installed first. After that, our environment may be set up by:
conda create -n DSSRNN python=3.6.9
conda activate DSSRNN
pip install -r requirements.txt
The data is available in the ./dataset directory.
As mentioned before, all the available training scripts are located in ./scripts.
For example:
To train the DSSRNN on ds44, you can use the script scripts/co2.sh:
sh scripts/co2.sh
It will start to train DSSRNN by default, the results will be shown in logs/LongForecasting. You can specify the name of the model in the script. (SSRNN, Linear, DLinear, NLinear)
There are other scripts available in scripts and The default look-back window in scripts is 96.
Other scripts can also be used using their respective directory. Additionally, you can cd FEDformer to run the scripts related to FEDfomer.
Our DSSRNN model has been rigorously tested against various benchmarks and has shown significant improvements in both short-term and long-term forecasting tasks.
- Performance Metrics:
- Computational Efficiency:
