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 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:
