Summary of Windowmixer: Intra-window and Inter-window Modeling For Time Series Forecasting, by Quangao Liu et al.
WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting
by Quangao Liu, Ruiqi Li, Maowei Jiang, Wei Yang, Chen Liang, LongLong Pang, Zhuozhang Zou
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the WindowMixer model, a novel approach to time series forecasting that addresses common challenges like noise, outliers, and missing values. Traditional methods model point-to-point relationships, but this can limit their ability to capture complex temporal patterns. The WindowMixer model is built on an all-MLP framework and leverages the continuous nature of time series by examining temporal variations from a window-based perspective. It decomposes time series into trend and seasonal components, handling them individually using fully connected layers for trends and intra-window and inter-window mixers for seasonal components. This approach captures intricate patterns and long-range dependencies in time series data. The WindowMixer model consistently outperforms existing methods in both long-term and short-term forecasting tasks. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict what will happen next in a sequence of numbers, like stock prices or weather forecasts. Right now, it’s hard to make accurate predictions because real-world data often has noise, errors, and missing values. The old way of doing things was to focus on short periods of time and try to find patterns there. But this new approach looks at the big picture and tries to understand how things change over time. It breaks down the sequence into smaller parts, like trends and seasonal patterns, and uses special math techniques to make predictions. This method is really good at predicting what will happen in both short-term and long-term forecasts. |
Keywords
» Artificial intelligence » Time series