Summary of Lms-autotsf: Learnable Multi-scale Decomposition and Integrated Autocorrelation For Time Series Forecasting, by Ibrahim Delibasoglu and Sanjay Chakraborty and Fredrik Heintz
LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
by Ibrahim Delibasoglu, Sanjay Chakraborty, Fredrik Heintz
First submitted to arxiv on: 9 Dec 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 proposed LMS-AutoTSF architecture is a novel time series forecasting approach that leverages dual encoders operating at multiple scales to capture both trends and seasonal variations. Unlike traditional models, LMS-AutoTSF employs learnable filters for low-pass and high-pass filtering, allowing it to dynamically adapt and isolate trend and seasonal components in the frequency domain. The model also integrates autocorrelation by computing lagged differences in time steps, enabling it to capture dependencies across time more effectively. Each encoder processes the input through fully connected layers to handle temporal and channel interactions. By combining these features, LMS-AutoTSF accurately captures long-term dependencies and fine-grained patterns while operating efficiently compared to other state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LMS-AutoTSF is a new way to forecast what will happen in the future based on past data. It’s like trying to guess what the weather will be tomorrow or next week. The model uses two special filters to help it understand what’s happening over time and also looks at how things are connected across different times. This helps it make more accurate predictions. The model is also very good at finding patterns in the data, which makes it useful for many applications like predicting stock prices or analyzing industrial processes. |
Keywords
» Artificial intelligence » Encoder » Time series
