Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting

This is the Pytorch implementation of CTLPE in the following paper: Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting, on the model Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Figure 1. The overview of CTLPE.

Requirements

• Python 3.6
• matplotlib == 3.1.1
• numpy == 1.19.4
• pandas == 0.25.1
• scikit_learn == 0.21.3
• torch == 1.8.0

Dependencies can be installed using the following command:

pip install -r requirements.txt

Usage

Commands for training and testing the model on Dataset ETTh1, ETTh2 and ETTm1 respectively:

# ETTh1
python -u main_informer.py --model informer --data ETTh1 --attn prob --freq h

# ETTh2
python -u main_informer.py --model informer --data ETTh2 --attn prob --freq h

# ETTm1
python -u main_informer.py --model informer --data ETTm1 --attn prob --freq t

More parameter information please refer to main_informer.py.