Jianxin Feng*, Jianhao Zhang, Ge Cao, Zhiguo Liu, Yuanming Ding
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1049-1068, 2025, DOI:10.32604/cmc.2024.058374
- 03 January 2025
Abstract Multivariate time series forecasting is widely used in traffic planning, weather forecasting, and energy consumption. Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series. However, the decomposition kernel of previous decomposition-based models is fixed, and these models have not considered the differences in frequency fluctuations between components. These problems make it difficult to analyze the intricate temporal variations of real-world time series. In this paper, we propose a series decomposition-based Mamba model, DecMamba, to obtain the intricate temporal dependencies and… More >
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