Summary of Rethinking Channel Dependence For Multivariate Time Series Forecasting: Learning From Leading Indicators, by Lifan Zhao et al.
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
by Lifan Zhao, Yanyan Shen
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 method, LIFT, addresses the issue of channel-independent methods in multivariate time series (MTS) forecasting by exploiting locally stationary lead-lag relationships between variates. It estimates leading indicators and their leading steps at each time step, allowing lagged variates to utilize advance information from these indicators. This plugin can be seamlessly collaborated with arbitrary time series forecasting methods. Experimental results on six real-world datasets show that LIFT improves the state-of-the-art methods by 5.5% in average forecasting performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LIFT is a new method for improving MTS forecasting. It works by finding patterns where some variables follow others, and then using this information to make better predictions. This helps by reducing the difficulty of predicting some variables because we can use earlier data from other variables that are leading indicators. LIFT can be used with any existing forecasting method and it improves performance by a small but significant amount. |
Keywords
* Artificial intelligence * Time series
