Running Inference with TimesFM

Original Repository | Paper

Follow these steps to set up and run inference using TimesFM:

1. Set up the environment.
2. Run the inference script with the following commands:

MODEL='timesfm'
for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2'; do
    for CTX_LEN in 96; do
        for PRED_LEN in 24 48 96 192 336 720; do
            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
                --data.data_manager.init_args.path ${DATA_DIR} \
                --trainer.default_root_dir ${LOG_DIR} \
                --data.data_manager.init_args.split_val true \
                --data.data_manager.init_args.dataset ${DATASET} \
                --data.data_manager.init_args.context_length ${CTX_LEN} \
                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
                --data.test_batch_size 64
        done
    done
done

Hyper-param in Inference

frequency (default: 0): Chose from {0, 1, 2}.

• 0 (default): High frequency, long horizon time series. We recommend using this for time series up to daily granularity.
• 1: Medium frequency time series. We recommend using this for weekly and monthly data.
• 2: Low frequency, short horizon time series. We recommend using this for anything beyond monthly, e.g., quarterly or yearly.

window size (default: None): Window size of trend + residual decomposition