Time Series Lightweight Adaptive Network (TSLANet) for Time Series Forecasting | TickerTrends.io
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Original PDF: https://arxiv.org/pdf/2404.08472.pdf
Author(s): Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Xiaoli Li
Abstract:
Recent advancements in time series forecasting have highlighted the potential of Transformer-based models due to their ability to capture long-range dependencies. However, challenges such as sensitivity to noise, computational inefficiency, and a tendency to overfit on smaller datasets limit their practical application. These inefficiencies led to the introduction of Time Series Lightweight Adaptive Network (TSLANet), a novel architecture designed to address these issues. TSLANet incorporates two main components: Adaptive Spectral Block (ASB) and Interactive Convolution Block (ICB), enhancing its noise resilience and computational efficiency, making it well-suited for various time series tasks across different datasets.
Introduction:
Traditional models like Convolutional Neural Networks (CNNs) and Transformers often struggle with long-range dependencies and short-term noise sensitivity. Transformer-based models, while a good initial start, suffer from significant drawbacks when dealing with smaller datasets. Its large parameter size often makes it vulnerable to overfitting and computational inefficiency. CNNs, while doing well on short-term pattern recognition in time series data, have varied results with different data frequencies (for example, varied performance on data in seconds vs data in hours). To overcome these limitations, TSLANet is proposed as a viable alternative framework that builds on traditional models. It leverages unique components to improve feature representation and robustness.
TSLAnet proposes a multi-block design of the Transformer which allows scalability as the computationally expensive self-attention gets replaced with a lightweight Adaptive Spectral Block (ASB) method. The ASB captures both short-term and long-term interactions within the data and attenuates high frequencies, minimizing noise and enhancing the clarity of the signal. This method leverages Fourier transforms in local and global filters to cover the whole frequency spectrum. Next, Interactive Convolution Block (ICB) helps learn the intricate spatial and temporal features within data.
TSLANet Architecture:
Taking the best properties from both Transformer-based models and CNNs, TSLANet creates a universal convolutional-based architecture, adept at handling various time series tasks through adaptive spectral feature extraction. This approach both allows for local feature learning capabilities as well as global temporal pattern recognition, offering a balanced solution for both local and long-range time series data.
The TSLANet Architecture relies on Discrete Fourier Transform (DFT) to transform a time series into a frequency domain representation. The choice of DFT in TSLANet was made for two main reasons: its discrete nature aligns well with digital processing and the existence of efficient computation methods.
TSLANet uses the following architecture to capture the temporal and spatial relationships within the time series data effectively:
Adaptive Spectral Block (ASB):
On a high level, this block aims to learn spatial information with the global circular convolution operations as well as create a local noise filtering system to not overfit to short-term noise in time series data.
Firstly, ASB relies on Fourier transforms for computation. With the Fourier transform, each channel of the time series is independently transformed, resulting in a comprehensive frequency domain representation that encapsulates the spectral characteristics of the original time series across all channels. An adaptive local filter allows the model to dynamically adjust the level of filtering according to dataset characteristics and remove these high-frequency noisy components. This is crucial when dealing with non-stationary data, where the frequency spectrum may change over time. Frequencies above a set threshold are kept while others are removed, giving the model flexibility in handling a wide range of data scenarios, and focusing only on the signal (versus the noise) in a frequency spectrum.
After adaptively filtering the frequency domain data, the model employs two sets of learnable filters; a global filter to learn from the original frequency domain data and a local filter to learn from the adaptively filtered data. These learnable filters are integrated together to capture the comprehensive spectral detail. Inverse Fourier Transform brings back this integrated frequency domain data back to the time series domain with all of the appropriate embeddings. This ASB method allows TSLANet to effectively capture both long-term and short-term interactions in time series data.
Interactive Convolution Block (ICB):
The design of the ICB includes parallel convolutions with different kernel sizes to capture local features and longer range dependencies in time series data. Specifically, the first convolutional layer is designed to capture fine-grained, localized patterns in the data with a smaller kernel. In contrast, the second layer aims to identify broader, longer-range dependencies with a larger kernel. The output represents the enhanced features ready for the final layer in the network. Overall the ICB block leverages self-supervised learning techniques to iteratively improve its predictive accuracy, ensuring robust performance even on fluctuating dataset sizes and unique noise levels.
Self-Supervised Learning Strategy:
TSLANet’s use of self-supervised learning involves training the model to predict unseen portions of input data without explicit external labels. This method not only improves generalization capabilities but also enhances the model’s ability to perform under different kinds of noise conditions and dataset characteristics.
Experiments and Results:
The efficacy of TSLANet on time series data can be tested on various types of tasks, such as classification, multivariate forecasting, and anomaly detection. The TSLANet algorithm can serve as a foundation model with competitive performance on each of these tasks as shown, however it does best on classification and anomaly detection.
Conclusion:
The results show that TSLANet achieves superior performance metrics compared to traditional Transformer-based models and CNNs, especially in environments characterized by high noise and smaller datasets. TSLANet certainly represents an advancement in time series forecasting technology. Its innovative use of Fourier analysis and convolutional blocks, coupled with self-supervised learning, allows it to surpass many current models in handling a wide range of conditions. Future work, developments and enhancements to the TSLANet architecture will be exciting to follow along with.
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