Summary of Test Time Learning For Time Series Forecasting, by Panayiotis Christou et al.
Test Time Learning for Time Series Forecasting
by Panayiotis Christou, Shichu Chen, Xupeng Chen, Parijat Dube
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a novel approach to time-series forecasting that leverages state-space models (SSMs) like Mamba, which utilize linear recurrent neural networks (RNNs) to model long-range dependencies in time-series data. The proposed method aims to improve the accuracy and scalability of token prediction mechanisms like multi-head attention, which have traditionally struggled with quadratic computational costs and complexity in capturing long-range dependencies. By incorporating linear RNNs and larger context windows, the SSM-based approach seeks to enhance the performance of time-series forecasting models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making it easier to predict what will happen next in a series of numbers, like stock prices or weather forecasts. It uses special types of computer programs called state-space models to help make these predictions more accurate and faster. The goal is to improve on current methods that are good at predicting short-term patterns but struggle with longer-term ones. |
Keywords
» Artificial intelligence » Multi head attention » Time series » Token
