# Time Series Classification with Shapelet and Canonical Features
## 1 Introduction
As a result, RSFCF limits the scope of Shapelet, which further improves the efficiency of Shapelet matching while retaining Shapelet's location information to a large extent. [PERSON] et al. proposed 22 typical time series features (\"Catch22\" for short; see Section 3.3 for details), including statistical features, spectral features and other types of features [15]. In order to improve the applicability of the algorithm and compensate for the accuracy loss caused by the random selection of Shapelet, this paper combines the Shapelet transformation technology [16] with multiple typical time series features,  
Figure 1: An example of mining discriminative information of subsequences via embedding canonical time series features. and proposes a random Shapelet transformation method that embeds typical time series features, and the final random forest classification model is constructed based on the new feature representation of the data. As in the example shown in Figure 1, the \"triangle\" causes the power spectrum of the subsequence to have a stronger response in the lower frequency band. Based on the eigenvalue of the subsequence on the feature SP_Summaries_welch_rect_area_5_1 (the sum of the energies of the five lowest frequencies in the Fourier power spectrum, which is one of the features in the Catch22), the two categories can be correctly distinguished (as shown in Figure 1e). In this case, although the candidate Shapelet S is not precise (i.e., contains a lot of random noise in addition to the \"triangle\"), we are still able to tap into the discriminatory information contained therein and rely on it for accurate classification. In order to fully verify the performance of the RSFCF algorithm proposed in this paper, we have conducted extensive experimental comparison and analysis with a number of current advanced time series classification algorithms on a large number of UCR time series datasets [7]. Experimental results on 112 datasets show that: (1) embedding typical time series features can effectively improve the accuracy of random Shapelet forests; (2) RSFCF surpasses the STC algorithm based on Shapelet precision search and Shapelet transformation techniques [17] in terms of accuracy (a recent work by [PERSON] et al. [18] showed that STC is the most accurate time series classification algorithm based on Shapelet), and is an order of magnitude faster than it is in training; (3) besides the STC algorithm, RSFCF surpasses many other types of advanced time series classification algorithms in terms of accuracy, including residual neural networks (ResNet) [19], Proximity Forest [5], and Canonical Interval Forests (CIF) [3]. The main contributions in this paper are summarized as follows:  
(1) Considering the characteristics of a real dataset, a method that can effectively improve the matching efficiency of Shapelet without a significant loss of accuracy is verified to limit the scope of Shapelet;  
(2) A novel method of embedding typical time series features in Shapelet is proposed, and experimental results show that this method can effectively compensate for the loss of accuracy caused by the random selection of Shapelet;  
(3) Based on the above method, an accurate and efficient time series classification algorithm is proposed--Random Shapelet Forest embedded with Canonical Time Series features (referred to as RSFCF). The rest of this article is organized as follows. Section 2 introduces the relevant work and recent progress made in time series classification, Section 3 introduces the relevant definitions and background knowledge, Section 4 describes the RSFCF algorithm proposed in this paper in detail, Section 5 verifies the performance of the RSFCF algorithm through extensive experimental comparison and analysis, and a final summary and possible future work are given in Section 6.