CCD: Capturing Cross Correlations with Deformable Convolutional Networks for Multivariate Time Series Forecasting

This code is the official PyTorch implementation of paper: CCD: Capturing Cross Correlations with Deformable Convolutional Networks for Multivariate Time Series Forecasting

Introduction

In this study, we address these challenges by proposing a gen- eral framework called CCD, which Capturing Cross Correlations with Deformable convolutional networks for multivariate time series Forecasting. First, we design Adaptive 2-Dimensions Shuffle, which adaptively reorders the rows and columns of the image-like structures, grouping potentially dependent patches to establish lo cal continuity. Second, we propose Cross Deformable Convolution, which is enhanced by Non-uniform Cross-Extension initialization and Dynamic mask-based modulation. Collectively, these designs render the module more adapted to temporal image-like structures, empowering it to capture complex and sparse cross correlations effectively. Extensive experiments on real-world datasets demon- strate the state-of-the-art performance of CCD.

Quickstart

Important

this project is fully tested under python 3.8, it is recommended that you set the Python version to 3.8.

1. Requirements

Given a python environment (note: this project is fully tested under python 3.8), install the dependencies with the following command:

pip install -r requirements.txt

2. Data preparation

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

3. Train and evaluate model

• We provide all the experiment scripts for CCD and other baselines under the folder ./scripts/multivariate_forecast/CCD. For example you can reproduce all the experiment results as the following script:

sh ./scripts/CCD/*.sh

Reproducibility

We provide all the train logs in ./log and all the result in ./result