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Attention based Multi-Modal New Product Sales Time-series Forecasting

An unofficial Pytorch implementation of Attention based Multi-Modal New Product Sales Time-series Forecasting paper. We use multiple approaches from this code and the aforementioned paper in our work Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends

The repository contains the implementation of the following baselines:

• KNN baselines:
  • Attribute KNN
  • Image KNN
  • Attribute + Image KNN
• Networks:
  • Image RNN
  • Concat Multimodal RNN
  • Residual Multimodal RNN
  • Explainable Cross-Attention RNN

Thanks to Nicholas Merci and Carlo Veronesi for the faithful implementation of the paper.

Installation

We suggest the use of VirtualEnv.

python3 -m venv mmrnn_venv
source mmrnn_venv/bin/activate
# mmrnn_venv\Scripts\activate.bat # If you're running on Windows

pip install numpy pandas matplotlib opencv-python permetrics Pillow scikit-image scikit-learn scipy tqdm transformers fairseq wandb

pip install torch torchvision

#For CUDA11.1 (NVIDIA 3K Serie GPUs)
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

export INSTALL_DIR=$PWD

cd $INSTALL_DIR
git clone https://github.com/HumaticsLAB/AttentionBasedMultiModalRNN.git
cd AttentionBasedMultiModalRNN
mkdir dataset

unset INSTALL_DIR

Dataset

VISUELLE dataset is publicly available to download here. Please download and extract it inside the dataset folder.

Run KNNs

To run the KNN models o please use the following scripts. Please check the arguments inside config.py and the dedicated arguments inside the script.

python KNN.py --exp_num 1 # Attribute KNN
python KNN.py --exp_num 2 # Image KNN
python KNN.py --exp_num 3 # Attribute+Image KNN

Training

To train the model of the baselines please use the following scripts. Please check the arguments inside config.py

python train.py 

Inference

To evaluate the model of the baselines please use the following scripts. Please check the arguments inside config.py

python infer.py

Citation

If you use VISUELLE dataset or this paper implementation, please cite the following papers.

@misc{skenderi2021googled,
      title={Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends}, 
      author={Geri Skenderi and Christian Joppi and Matteo Denitto and Marco Cristani},
      year={2021},
      eprint={2109.09824},
}

@inbook{10.1145/3394486.3403362,
author = {Ekambaram, Vijay and Manglik, Kushagra and Mukherjee, Sumanta and Sajja, Surya Shravan Kumar and Dwivedi, Satyam and Raykar, Vikas},
title = {Attention Based Multi-Modal New Product Sales Time-Series Forecasting},
year = {2020},
isbn = {9781450379984},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394486.3403362},
booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {3110–3118},
numpages = {9}
}