A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting
This work addresses computational efficiency and accuracy issues in time series forecasting for applications like finance or weather prediction, representing an incremental improvement over existing methods.
The paper tackled the challenge of improving Transformer models for long-term multivariate time series forecasting with lower computational costs by introducing the Joint Time-Frequency Domain Transformer (JTFT), which achieved superior predictive performance over state-of-the-art baselines on six real-world datasets.
In order to enhance the performance of Transformer models for long-term multivariate forecasting while minimizing computational demands, this paper introduces the Joint Time-Frequency Domain Transformer (JTFT). JTFT combines time and frequency domain representations to make predictions. The frequency domain representation efficiently extracts multi-scale dependencies while maintaining sparsity by utilizing a small number of learnable frequencies. Simultaneously, the time domain (TD) representation is derived from a fixed number of the most recent data points, strengthening the modeling of local relationships and mitigating the effects of non-stationarity. Importantly, the length of the representation remains independent of the input sequence length, enabling JTFT to achieve linear computational complexity. Furthermore, a low-rank attention layer is proposed to efficiently capture cross-dimensional dependencies, thus preventing performance degradation resulting from the entanglement of temporal and channel-wise modeling. Experimental results on six real-world datasets demonstrate that JTFT outperforms state-of-the-art baselines in predictive performance.