xLSTMTime : Long-term Time Series Forecasting With xLSTM
(2407.10240)Abstract
In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The emergence of LTSF-Linear, with its straightforward linear architecture, has notably outperformed transformer-based counterparts, prompting a reevaluation of the transformer's utility in time series forecasting. In response, this paper presents an adaptation of a recent architecture termed extended LSTM (xLSTM) for LTSF. xLSTM incorporates exponential gating and a revised memory structure with higher capacity that has good potential for LTSF. Our adopted architecture for LTSF termed as xLSTMTime surpasses current approaches. We compare xLSTMTime's performance against various state-of-the-art models across multiple real-world da-tasets, demonstrating superior forecasting capabilities. Our findings suggest that refined recurrent architectures can offer competitive alternatives to transformer-based models in LTSF tasks, po-tentially redefining the landscape of time series forecasting.
Overview
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The xLSTMTime model leverages the advanced capabilities of the xLSTM architecture, which incorporates exponential gating and augmented memory structures to improve the stability and scalability of time series forecasting models.
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The model successfully addresses the challenges posed by transformer-based models, such as high computational costs and difficulty in capturing complex temporal dynamics, by enhancing recurrent modules like LSTM with advanced components such as series decomposition and normalization techniques.
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Experimental results on 12 real-world datasets demonstrate xLSTMTime's superior performance in capturing long-term dependencies and complex temporal patterns, consistently outperforming state-of-the-art models like PatchTST and DLinear.
Long-Term Time Series Forecasting with xLSTM: A Critical Analysis
The paper "xLSTMTime: Long-term Time Series Forecasting With xLSTM" by Musleh Alharthi and Ausif Mahmood addresses the current trend in time series forecasting, particularly in the context of multivariate long-term time series forecasting (LTSF). The paper highlights a pertinent issue: while transformer-based models have substantially advanced the field, they come with their own set of challenges, including high computational costs and difficulty in capturing complex temporal dynamics over extended sequences.
Background and Motivations
Historically, time series forecasting has leveraged statistical models like SARIMA and TBATs, as well as machine learning techniques such as Linear Regression and XGBoost. With the ascendancy of deep learning, RNN variants like LSTM and GRU, followed by CNNs, have been employed extensively. In recent years, transformer-based architectures, originally successful in NLP, have been repurposed for time series forecasting. Popular models include Informer, Autoformer, FEDformer, and more recent innovations integrating techniques from state-space models and modular blocks.
However, the simplicity and surprising efficacy of models like LTSF-Linear has challenged the prevailing assumption that complex architectures result in better performance. This insight motivates the exploration of improved recurrent architectures, leading to the development of the xLSTMTime model.
The xLSTMTime Model
The xLSTMTime model adapts the recent advances in the xLSTM architecture for time series forecasting. The xLSTM architecture, originally designed to enhance traditional LSTM models, incorporates exponential gating and augmented memory structures, which improve stability and scalability. xLSTM's two variants—sLSTM and mLSTM—offer enhancements tailored to different data scales and complexities.
Key Components
Series Decomposition:
- The input time series is decomposed into trend and seasonal components using 1-D convolutions, enhancing the model’s ability to capture periodic and long-term trends.
Batch and Instance Normalization:
- Batch Normalization is applied to stabilize learning, while Instance Normalization ensures that the input feature maps maintain a mean of zero and variance of one, thereby enhancing stability and convergence during training.
sLSTM and mLSTM Modules:
- The sLSTM variant is used for smaller datasets, leveraging scalar memory and exponential gating to handle long-term dependencies.
- The mLSTM variant, suitable for larger datasets, employs a matrix memory cell to enhance storage capacity and retrieval efficiency, facilitating more complex sequence modeling.
Experimental Results
The performance of the xLSTMTime model was evaluated on 12 widely used real-world datasets, covering diverse domains such as weather, traffic, electricity, and health records. The results are compelling, with xLSTMTime outperforming state-of-the-art models like PatchTST, DLinear, FEDformer, and others across most benchmarks.
Specific numerical results include:
- Significant MAE and MSE improvements on the Weather dataset (e.g., 18.18% improvement over DLinear for T=96).
- Consistent superiority on multivariate tasks in the PeMS datasets, often achieving the best or second-best results.
Visual comparisons of predicted versus actual values (Figures 4 and 5) illustrate the model’s proficiency in capturing data periodicity and variation accurately.
Discussion
The comparative analysis reveals that xLSTMTime delivers robust performance, particularly for datasets characterized by complex temporal patterns. Notably, xLSTMTime's advantage is pronounced at longer prediction horizons, likely due to its enhanced memory capacity and series decomposition strategy.
Whereas DLinear and PatchTST have their strengths, xLSTMTime consistently shows better results on intricate datasets, highlighting the importance of refined recurrent modules in LTSF. The model's competitive edge in many benchmarks underscores the potential of revisiting and enhancing traditional RNN-based architectures like LSTM.
Conclusions and Future Directions
The xLSTMTime model demonstrates a successful adaptation of xLSTM architecture to the time series forecasting domain. By integrating advanced gating mechanisms, memory structures, and normalization techniques, xLSTMTime achieves notable improvements over both transformer-based and simpler linear models.
These findings advocate for further exploration into enhanced recurrent architectures for time series forecasting. Future developments could aim to streamline these models for even greater efficiency or investigate hybrid models that combine the strengths of transformers and recurrent networks.
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