Summary of Lipcot: Linear Predictive Coding Based Tokenizer For Self-supervised Learning Of Time Series Data Via Language Models, by Md Fahim Anjum
LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language Models
by Md Fahim Anjum
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LiPCoT tokenizer enables self-supervised learning of time series data using existing Language model architectures like BERT. It encodes time series into a sequence of tokens through linear predictive coding, creating a latent space that captures the stochastic nature of the data. This compact yet rich representation overcomes limitations of traditional tokenizers and can handle varying sampling rates and lengths. The effectiveness of LiPCoT is demonstrated in classifying Parkinson’s disease using an EEG dataset from 46 participants. By encoding EEG data into tokens and using BERT for self-supervised learning, LiPCoT-based models outperform state-of-the-art CNN-based architectures by significant margins (7.1% precision, 2.3% recall, 5.5% accuracy, 4% AUC, 5% F1-score). This work has implications for foundational models of time series and self-supervised learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LiPCoT is a new way to understand time series data using language models like BERT. It takes time series data and breaks it down into smaller pieces called tokens. These tokens are then used to train the model without needing labeled training data. This method was tested on EEG data from people with Parkinson’s disease and showed better results than other methods. |
Keywords
» Artificial intelligence » Auc » Bert » Cnn » F1 score » Language model » Latent space » Precision » Recall » Self supervised » Time series » Tokenizer
