Summary of Unlocking the Power Of Lstm For Long Term Time Series Forecasting, by Yaxuan Kong et al.
Unlocking the Power of LSTM for Long Term Time Series Forecasting
by Yaxuan Kong, Zepu Wang, Yuqi Nie, Tian Zhou, Stefan Zohren, Yuxuan Liang, Peng Sun, Qingsong Wen
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 proposes a new algorithm called P-sLSTM for time series forecasting (TSF) tasks. The authors build upon recent advances in natural language processing (NLP) by modifying the recently introduced sLSTM architecture. They incorporate patching and channel independence to address the potential short memory issue of sLSTM, allowing it to be applied directly in TSF. The modifications substantially enhance sLSTM’s performance, achieving state-of-the-art results. The paper provides theoretical justifications for the design and conducts extensive experiments to validate its efficiency and superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The authors are trying to improve a type of artificial intelligence called recurrent neural networks (RNNs) that are used to predict future events based on past data. They take an RNN model from the field of language processing and modify it so it can be used for predicting time series data, like stock prices or weather patterns. This new version is called P-sLSTM and does a better job than previous versions at making accurate predictions. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Rnn » Time series
