PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
This addresses forecasting accuracy and efficiency for indoor air quality monitoring, but appears incremental as it builds on existing decomposition and RNN methods.
The paper tackles time series forecasting by proposing PIAD-SRNN, a physics-informed adaptive decomposition state-space RNN that separates seasonal and trend components and embeds domain equations, and results show it consistently outperforms state-of-the-art models in terms of MSE and MAE on indoor air quality datasets.
Time series forecasting often demands a trade-off between accuracy and efficiency. While recent Transformer models have improved forecasting capabilities, they come with high computational costs. Linear-based models have shown better accuracy than Transformers but still fall short of ideal performance. We propose PIAD-SRNN, a physics-informed adaptive decomposition state-space RNN, that separates seasonal and trend components and embeds domain equations in a recurrent framework. We evaluate PIAD-SRNN's performance on indoor air quality datasets, focusing on CO2 concentration prediction across various forecasting horizons, and results demonstrate that it consistently outperforms SoTA models in both long-term and short-term time series forecasting, including transformer-based architectures, in terms of both MSE and MAE. Besides proposing PIAD-SRNN which balances accuracy with efficiency, this paper also provides four curated datasets. Code and data: https://github.com/ahmad-shirazi/DSSRNN