absaw/DDM_Timeseries_Forecast
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series to benchmark datasets from different domains
This project helps you predict future values for complex, multi-faceted time series data, like hospital patient flow, solar energy output, or macroeconomic indicators. It takes your historical, multi-variable time series data and generates a range of probable future outcomes, rather than just a single prediction. This is useful for analysts, researchers, or planners in fields dealing with uncertain, evolving systems.
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Use this if you need robust, probabilistic forecasts for multivariate time series in domains like healthcare, environmental science, or finance, where understanding the range of possible futures is critical.
Not ideal if you only need a simple, single-point forecast for a univariate time series or if your data is not time-dependent.
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Last pushed
Apr 28, 2023
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