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Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting"
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| 2026-07-31 | 256 |
| 2026-08-05 | 256 |
| 2026-08-06 | 256 |
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# TSDiff: An Unconditional Diffusion Model for Time Series [](https://arxiv.org/abs/2307.11494) [](https://opensource.org/licenses/Apache-2.0) [](https://neurips.cc/) <p align="center"> <img src="./assets/overview.png" width="100%"> <br /> <span>Fig. 1: An overview of TSDiff’s use cases. <b>Predict:</b> By utilizing observation self-guidance, TSDiff can be conditioned during inference to perform predictive tasks such as forecasting. <b>Refine:</b> Predictions of base forecasters can be improved by leveraging the implicit probability density of TSDiff. <b>Synthesize:</b> Realistic samples generated by TSDiff can be used to train downstream forecasters achieving good performance on real test data.</span> </p> --- This repository contains the official implementation of the NeurIPS 2023 paper [*Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting*](https://arxiv.org/abs/2307.11494). In this paper, we propose *TSDiff*, an unconditional diffusion model for time series. Our proposed self-guidance mechanism enables conditioning TSDiff for downstream tasks during inference, without requiring auxiliary networks or altering the training procedure. Furthermore, our refinement scheme leverages the implicit density learned by the diffusion model to iteratively refine the predictions of base forecasters. Finally, we demonstrate the high quality of the synthetic time series by training downstrain models solely on generated data and introducing the *Linear Predictive Score (LPS)*. <p align="center"> <img src="./assets/forecasts.png" width="60%"> <br /> <span>Fig. 2: Example forecasts generated by TSDiff-Q for time series in Electricity, KDDCup, and Exchange — three datasets with different frequencies and/or prediction lengths.</span> </p> ## Installation TSDiff requires Python 3.8 or higher. * Create a conda environment (optional, but recommended). ```sh conda create --name tsdiff --yes python=3.8 && conda activate tsdiff ``` * Install this package. ```sh pip install --editable "." ``` > [!TIP] > We have some updates in the `update` branch. If you're interested in testing out TSDiff or [training it on a custom dataset](https://github.com/amazon-science/unconditional-time-series-diffusion/issues/7), using the `update` branch maybe faster for training. ## Usage ### Training Models Train models using the `train_model.py` and `train_cond_model.py` scripts for `TSDiff` and `TSDiff-Cond`, respectively. Sample configurations can be found in `configs/train_tsdiff.yaml` and `configs/train_tsdiff-cond.yaml`. Specific configurations used in the paper can be found in `configs/train_tsdiff` and `configs/train_tsdiff-cond`. Example commands for regular (i.e., no missing values) forecasting: ```sh # Train TSDiff on the Uber dataset for regular forecasting python bin/train_model.py -c configs/train_tsdiff/train_uber_tlc.yaml # Train TSDiff on the M4 dataset for regular forecasting python bin/train_model.py -c configs/train_tsdiff/train_m4.yaml # Train TSDiff-Cond on the Uber dataset for regular forecasting python bin/train_cond_model.py -c configs/train_tsdiff-cond/uber_tlc_hourly.yaml # Train TSDiff-Cond on the M4 dataset for regular forecasting python bin/train_cond_model.py -c configs/train_tsdiff-cond/m4_hourly.yaml ``` Example commands for forecasting with missing values: ```sh # Train TSDiff on the Uber dataset for the missing values experiment python bin/train_model.py -c configs/train_tsdiff/train_missing_uber_tlc.yaml # Train TSDiff on the KDDCup dataset for the missing values experiment python bin/train_model.py -c configs/train_tsdiff/train_missing_kdd_cup.yaml # Train TSDiff-Cond on the Uber dataset for the RM missing values experim
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