TapNet

This is a Pytorch implementation of Attentional Prototype Network for the task of (semi-supervised) classification of multivariate time series, as described in our AAAI 2020 paper:

TapNet: Multivariate Time Series Classification with Attentional Prototype Network

Run the demo

python train.py

Data

We use the latest multivariate time series classification dataset from UAE archive with 30 datasets in wide range of applications.

The raw data is converted into npy data files in the following format:

• Training Samples (X_train.npy): an N by M by L tensor (N is the number of time series, M is the multivariate dimension, L is the length of time series),
• Train labels（y_train.npy）: an N by 1 vector (N is the number of time series)
• Testing Samples (X_test.npy): an N by M by L tensor (N is the number of time series, M is the multivariate dimension, L is the length of time series),
• Testing labels (y_test.npy): an N by 1 vector (N is the number of time series)

You can specify a dataset as follows:

python train.py --dataset NATOPS

(or by editing train.py)

The default data is located at './dataset'.