MMAML-TSR

Multi-modal meta-learning for time series regression

This repo contains the implementation of this paper which adapts MAML (Finn et al., 2017) and MMAML (Vuorio et al., 2019) to Time Series Regression.

The multimodal-meta-learning is based on this official implementation.

Dependencies

• Python 3.7.0
• Pytorch 1.4.0
• Learn2learn 0.1.1

Data

The code can be used on two open datasets that need to be pre-processed before running MAML or MMAML. The data is available on:

• Air Pollution Dataset: PM2.5 Data of Five Chinese Cities Data Set.

• Heart Rate Dataset: PPG-DaLiA Data Set.

Usage

1. Download data and create the following folder structure:

MMAML-TSR/
├── logs
	├── MAML_output/ 
	├── MMAML_output/
	...
├── data
├── code
	├── tools
	├── pre_processing
	├── models
	...

1. Preprocess and generate .pickle file

Change the paths to the raw data in the file pre_processing/ts_dataset.py accordingly. Then run pre_processing/dataset_creation.ipynb to pickle the object with the transformed data. For a new dataset, a loading functionality should be created by taking our datasets as reference. Optionally, you can download the preprocessed data HERE.

2. Run MAML

Assuming that the pickled files are in data/. Training with the default parameters on the Air Pollution Dataset works as:

cd code/
python run_MAML.py

To train on Heart-rate data:

cd code/
python run_MAML.py --dataset HR

3. Run MMAML

Assuming that the pickled files are in data/. Training with the default parameters on the Air Pollution Dataset works as:

cd code
python run_MMAML.py

To train on Heart-rate data:

cd code
python run_MMAML.py --dataset HR

Cite us

If this repository is useful, please cite us as:

@inproceedings{arango2021multimodal,
  title={Multimodal meta-learning for time series regression},
  author={Arango, Sebastian Pineda and Heinrich, Felix and Madhusudhanan, Kiran and Schmidt-Thieme, Lars},
  booktitle={Advanced Analytics and Learning on Temporal Data: 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers 6},
  pages={123--138},
  year={2021},
  organization={Springer}
}

Contact

To ask questions or report issues, please open an issue on the issues tracker.