The repo includes the detailed implementation for the paper: AutoTimes: Autoregressive Time Series Forecasters via Large Language Models.
You can download the datasets from other repositories like Autoformer.
Large language models can be downloaded from Hugging Face.
# the default large language model is LLaMA-7B
# long-term forecasting
bash ./scripts/time_series_forecasting/long_term/AutoTimes_ETTh1.sh
# short-term forecasting
bash ./scripts/time_series_forecasting/short_term/AutoTimes_M4.sh
# zero-shot forecasting
# it's worth noting that sM4_tM3 utilizes models trained
# on short-term, you should run AutoTimes_M4 first
bash ./scripts/zero_shot_forecasting/sM4_tM3.sh
bash ./scripts/zero_shot_forecasting/sM3_tM4.sh
# in-context forecasting
bash ./scripts/in_context_forecasting/M3.sh
# other large language models
bash ./scripts/method_generality/opt.sh
# preprocess timestamps to generate text embedding
python ./preprocess.py --gpu 0 --dataset ETTh1