MSP-EDA: Multivariate Time Series Forecasting Based on Multiscale Patches and External Data Augmentation

Accurate multivariate time series forecasting remains a major challenge in various real-world applications, primarily due to the limitations of existing models in capturing multiscale temporal dependencies and effectively integrating external data. To address these issues, we propose MSP-EDA, a novel multivariate time series forecasting framework that integrates multiscale patching and external data enhancement. Specifically, MSP-EDA utilizes the Discrete Fourier Transform (DFT) to extract dominant global periodic patterns and employs an adaptive Continuous Wavelet Transform (CWT) to capture scale-sensitive local variations. In addition, multiscale patches are constructed to capture temporal patterns at different resolutions, and a specialized encoder is designed for each scale. Each encoder incorporates temporal attention, channel correlation attention, and cross-attention with external data to capture intra-scale temporal dependencies, inter-variable correlations, and external influences, respectively. To fuse information from different temporal scales, we introduce a trainable global token that serves as a variable-wise aggregator across scales. Extensive experiments on four public benchmark datasets and three real-world vector database datasets that we collect demonstrate that MSP-EDA consistently outperforms state-of-the-art methods, achieving particularly notable improvements on vector database workloads. Ablation studies further confirm the effectiveness of each module and the adaptability of MSP-EDA to complex forecasting scenarios involving external dependencies.