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Arxiv
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Mamba time series forecasting with uncertainty quantification
- Mamba, a state space model, has gained attention for time series forecasting.
- Mamba forecasts in electricity consumption benchmarks show an average error of about 8%.
- In traffic occupancy benchmarks, the mean error in Mamba forecasts reaches 18%.
- A method is proposed to quantify the predictive uncertainty of Mamba forecasts.
- A dual-network framework based on the Mamba architecture is introduced for probabilistic forecasting.
- The framework includes one network for point forecasts and another for estimating predictive uncertainty by modeling variance.
- The tool is named Mamba-ProbTSF, and its implementation code is available on GitHub.
- Evaluation on synthetic and real-world benchmark datasets shows effectiveness.
- Kullback-Leibler divergence between learned distributions and data is reduced to a low level for both synthetic and real-world data.
- The true trajectory stays within the predicted uncertainty interval around 95% of the time for both electricity consumption and traffic occupancy benchmarks.
- Considerations for limitations, performance improvements, and applications to stochastic dynamics processes are discussed.
- The research is detailed in arXiv:2503.10873v2, focusing on time series forecasting with uncertainty quantification.
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