Summary of Xlstmtime : Long-term Time Series Forecasting with Xlstm, by Musleh Alharthi and Ausif Mahmood
xLSTMTime : Long-term Time Series Forecasting With xLSTM
by Musleh Alharthi, Ausif Mahmood
First submitted to arxiv on: 14 Jul 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 This paper explores the use of a novel architecture called extended LSTM (xLSTM) for multivariate long-term time series forecasting (LTSF). xLSTM is designed to overcome the limitations of transformer-based models, which are widely used in LTSF. The authors compare their proposed model, xLSTMTime, with state-of-the-art approaches across multiple real-world datasets and demonstrate superior performance. The findings suggest that refined recurrent architectures can be a viable alternative to transformer-based models in LTSF tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new kind of artificial intelligence (AI) model to predict what will happen in the future based on patterns from the past. It’s called extended LSTM, or xLSTM for short. The researchers wanted to see if this type of model could do better than some other popular AI models that are already being used for this task. They tested their new model against the others and found that it did a much better job at predicting what would happen in the future. This is an important discovery because it means we might have even more powerful tools to help us make predictions about things like weather, stock prices, or traffic. |
Keywords
* Artificial intelligence * Lstm * Time series * Transformer
