Time Series Classification Based on Multi-Dimensional Feature Fusion

Time series classification is a key problem in data mining, most of existing classification methods directly extract one-dimensional data from one-dimensional features, which cannot effectively express the inter-relation between different time points. Besides, some classification methods extract two-dimensional features through encoding raw one-dimensional data into two-dimensional images, and part of information is lost due to the difference of encoding methods. How to make full use of one-dimensional and two-dimensional features to extract valuable information and integrate them in an optimal fashion remains a promising challenge. In this paper, we propose a multi-scale convolutional network to extract one-dimensional features from time series for obtaining more feature information based on multi-scale convolution kernels. Two-dimensional features are constructed in terms of two-dimensional image coding based on
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ramian angular field,
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arkov transition field and
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ecurrence plot (GMR) methods. We develop a multi-dimensional feature fusion approach leveraging
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queeze-and-
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xcitation (SE) and
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elf-
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ttention (SA) mechanism to effective fusing one-dimensional multi-scale features and two-dimensional image features in terms of weight setting. We conduct experimental verification based on 84 complete data traces from a typical UCR dataset in the field. Experimental results show that the accuracy of our proposed approach improves by 3.35% compared with existing benchmark methods. The Grad ient-weighted
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lass
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ctivation
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apping (Grad-CAM) visualization analysis method is adopted, where our proposed approach extracts more accurate features and effectively distinguishes different time series data categories.