ProbeNet: Augmenting Time Series Forecasting with Future-Known Exogenous Variables via Patch-Local Exogenous-to-Target Modeling

Quickstart

Prepare Data

You can obtained the well pre-processed datasets from Google Drive. Then place the downloaded data under the folder ./dataset.

Installation

Important

this project is fully tested under python 3.11, it is recommended that you set the Python version to 3.11.

1. Clone this repository.

   git clone https://github.com/mallocobject/ProbeNet.git
   cd ProbeNet

2. Create a new Conda environment.

   conda create -n probe python=3.11
   conda activate probe

3. Install Core Dependencies

   ⚠️ CUDA Compatibility Notice The torch prebuilt package is CUDA-version specific. (See https://pytorch.org/get-started/previous-versions/) Please make sure to install the package that matches your local CUDA version (e.g., cu118 or cu121). Recommended: torch==2.5.1

   pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu121
   pip install -r requirements.txt

Train and Evaluate

• To see the model structure of ProbeNet, click here.
• We provide all the experiment scripts for ProbeNet and other baselines under the folder ./scripts/w_future. For example you can reproduce all the experiment results as the following script:

sh ./scripts/w_future/ProbeNet.sh