TOTEM: TOkenized Time series EMbeddings for General Time Series Analysis

TOTEM explores time series unification through discrete tokens (not patches!!). Its simple VQVAE backbone learns a self-supervised, discrete, codebook in either a generalist (multiple domains) or specialist (1 domain) manner. TOTEM's codebook can then be tested on in domain or zero shot data with many 🔥 time series tasks.

For a high level overview see the video recap. Check out the paper for more details!

Get Started with TOTEM 💪

1. Setup your environment 🤓

pip install -r requirements.txt

2. Get the data ⏳

3. Run TOTEM 🚀

# Imputation Specialist
imputation/scripts/electricity.sh or ETTh1.sh or ETTh2.sh or ETTm1.sh or ETTm2.sh or weather.sh

# Imputation Generalist
imputation/scripts/all.sh

# Anomaly Detection Specialist
anomaly_detection/scripts/msl.sh or psm.sh or smap.sh or smd.sh or swat.sh

# Anomaly Detection Generalist
anomaly_detection/scripts/all.sh

# Forecasting Specialist
forecasting/scripts/electricity.sh or ETTh1.sh or ETTh2.sh or ETTm1.sh or ETTm2.sh or weather.sh or traffic.sh

# Forecasting Generalist
forecasting/scripts/all.sh

# Process Zero Shot Data
process_zero_shot_data/scripts/neuro2.sh or neuro5.sh or saugeen.sh or sunspot.sh or us_births.sh

4. Model Zoo (a.k.a Pretrained Models) 🦑🐯🐊🐳

Find the pretrained generalist tokenizers here. Read some notes on usage here.

Cite If You ❤️ TOTEM

@article{
talukder2024totem,
title={{TOTEM}: {TO}kenized Time Series {EM}beddings for General Time Series Analysis},
author={Sabera J Talukder and Yisong Yue and Georgia Gkioxari},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=QlTLkH6xRC},
note={}
}