🚀 This is the official repo of "Non-stationary Diffusion For Probabilistic Time Series Forecasting"
- Weiwei Ye (equal contribution): [email protected]
- Zhuopeng Xu (equal contribution): [email protected]
- Ning gui (corresponding author): [email protected]
📌 [2025-05-01] 🎉🎉🎉🎉🎉 NsDiff is accepted as a Spotlight poster at ICML 2025 — Oral decision pending 🔎
NsDiff is a new diffusion-based theoretical framework for probalistic forecasting. Specifically designed for non-stationary scenarios.
pip install -r ./requirements.txt
see ./scripts/ for more examples.
- pretrain and run
# pretraining
bash ./scripts/pretrain_F/ETTh1.sh
# run
export PYTHONPATH=./
CUDA_DEVICE_ORDER=PCI_BUS_ID \
python3 ./src/experiments/NsDiff.py \
--dataset_type="ETTh1" \
--device="cuda:0" \
--batch_size=32 \
--horizon=1 \
--pred_len=192 \
--windows=168 \
--load_pretrain=True \
runs --seeds='[1232132, 3]'- run without pretraining
# run without pretraining
export PYTHONPATH=./
CUDA_DEVICE_ORDER=PCI_BUS_ID \
python3 ./src/experiments/NsDiff.py \
--dataset_type="ETTh1" \
--device="cuda:0" \
--batch_size=32 \
--horizon=1 \
--pred_len=192 \
--windows=168 \
runs --seeds='[1232132, 3]'