amazon-science/unconditional-time-series-diffusion
Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting"
This tool helps experts who work with time-based data to predict future trends, improve existing forecasts, or generate realistic synthetic data. You input your historical time series data, and it outputs probabilistic forecasts, refined predictions, or new synthetic datasets. It's ideal for data scientists, analysts, or researchers dealing with complex time series like electricity consumption, sales figures, or sensor readings.
248 stars. No commits in the last 6 months.
Use this if you need advanced, probabilistic forecasts for time series data, want to enhance the accuracy of your current prediction models, or require realistic synthetic data for training new models.
Not ideal if you only need simple, deterministic point forecasts or are working with non-sequential, static datasets.
Stars
248
Forks
36
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 30, 2024
Commits (30d)
0
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