XLSTMTime: Long-Term Time Series Forecasting with xLSTM
The paper introduces xLSTM, an architecture for long-term time series forecasting. It addresses transformer model challenges, showing superior performance in real-world datasets. Authors: Musleh Alharthi and Ausif Mahmood.
Read original article
The paper titled "xLSTMTime: Long-term Time Series Forecasting With xLSTM" introduces an extended LSTM (xLSTM) architecture for multivariate long-term time series forecasting (LTSF). This new model addresses challenges faced by transformer-based models, such as high computational demands and difficulty in capturing temporal dynamics. The xLSTM architecture incorporates exponential gating and a revised memory structure with higher capacity, showing promising potential for LTSF tasks. The study compares xLSTMTime's performance with state-of-the-art models across various real-world datasets, demonstrating superior forecasting capabilities. The findings suggest that refined recurrent architectures like xLSTMTime could provide competitive alternatives to transformer-based models in LTSF, potentially reshaping the landscape of time series forecasting. The paper is authored by Musleh Alharthi and Ausif Mahmood, and it is available for further exploration through the provided DOI link.
Related
xLSTM Explained in Detail
Maximillan Beck's YouTube video delves into XLSTM as a Transformer alternative in language modeling. XLSTM combines LSTM and modern techniques to tackle storage and decision-making issues, aiming to rival Transformers in predictive tasks.
The Illustrated Transformer
Jay Alammar's blog explores The Transformer model, highlighting its attention mechanism for faster training. It outperforms Google's NMT in some tasks, emphasizing parallelizability. The blog simplifies components like self-attention and multi-headed attention for better understanding.
Math Behind Transformers and LLMs
This post introduces transformers and large language models, focusing on OpenGPT-X and transformer architecture. It explains language models, training processes, computational demands, GPU usage, and the superiority of transformers in NLP.
Learning to (Learn at Test Time): RNNs with Expressive Hidden States
The paper introduces Test-Time Training (TTT) layers for sequence modeling, featuring linear complexity and self-supervised learning for training on test sequences. TTT-Linear outperforms Transformer, while TTT-MLP shows potential for long contexts.
Transformer Layers as Painters
The study "Transformer Layers as Painters" by Qi Sun et al. delves into transformer models, showcasing layer impact variations and potential for model optimization through strategic layer adjustments.
- Some professionals in economics and finance resonate with the value of LSTM models over transformers.
- Questions arise about the general superiority of transformer models compared to non-deep learning models.
- There is curiosity about the applicability of xLSTM to financial trading and event classification in time series.
- Skepticism exists regarding the effectiveness of published time series forecasting methods in unpredictable domains like the stock market.
- Some comments humorously misinterpret or criticize the potential misuse of the model in financial contexts.
11 commentsProminence, yes. But are they generally better than non-deep learning models? My understanding was that this is not the case, but I don't follow this field closely.
https://deepmind.google/discover/blog/graphcast-ai-model-for...
Related
xLSTM Explained in Detail
Maximillan Beck's YouTube video delves into XLSTM as a Transformer alternative in language modeling. XLSTM combines LSTM and modern techniques to tackle storage and decision-making issues, aiming to rival Transformers in predictive tasks.
The Illustrated Transformer
Jay Alammar's blog explores The Transformer model, highlighting its attention mechanism for faster training. It outperforms Google's NMT in some tasks, emphasizing parallelizability. The blog simplifies components like self-attention and multi-headed attention for better understanding.
Math Behind Transformers and LLMs
This post introduces transformers and large language models, focusing on OpenGPT-X and transformer architecture. It explains language models, training processes, computational demands, GPU usage, and the superiority of transformers in NLP.
Learning to (Learn at Test Time): RNNs with Expressive Hidden States
The paper introduces Test-Time Training (TTT) layers for sequence modeling, featuring linear complexity and self-supervised learning for training on test sequences. TTT-Linear outperforms Transformer, while TTT-MLP shows potential for long contexts.
Transformer Layers as Painters
The study "Transformer Layers as Painters" by Qi Sun et al. delves into transformer models, showcasing layer impact variations and potential for model optimization through strategic layer adjustments.