# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 2 Related Work  
### Related Work on TSC  
There are a variety of methods for the task of TSC. These methods can be divided into four main categories: distance-based methods, feature-based methods, ensemble methods, and deep learning methods. **Distance-based methods. ** This type of method uses a variety of distance functions [16] to measure the similarity between TS records for classification. The most widely used distance function methods are DTW and its variants, such as the one nearest neighbor (1 NN) classifier with DTW (1 NN-DTW) [2]. In [17], the authors proposed a framework named Proximity Forest (PF), which uses Proximity Trees with 11 distance measures for the TSC task. The main drawback of this kind of method is the huge computational cost involved. **Feature-based methods. ** The basis of this type of method is a variety of features learned from TS data, through which we can distinguish the differences between data and classify them. The methods in this class include ST [18; 19], bag of symbolic Fourier approximation (SFA) symbols (BOSS) [20], time-series forest (TSF) [21], and TS classification based on a bag-of-features representation (TSBF) [22]. Word ExtrAction for time SEries cLassification (WEASEL) [23] uses a novel discriminative feature generation and a feature selection method based on bag-of-patterns (BOP). The Shapelet Transform Classification (STC) uses a novel way to find shapelet and increases accuracy for multi-class problems [24]. Random Interval Spectral Ensemble (RISE) [25] combines tree structure with multiple features for TSC. Some methods input hand-engineered features using some domain knowledge into a DNN discriminative classifier. The disadvantages of these methods lie in the complexity and weak generality of building features, which obviously limits their versatility. Besides hand-engineered features, some methods use a DNN to extract the features of TS for classification: In [26; 27], the authors added a deconvolutional operation into a convolutional neural network (CNN)-based model to reconstructing a multivariate time-series. Deep Belief Networks (DBNs) [28] and Recurrent Neural Network auto-encoders [29] have also been used to model the latent features in an unsupervised manner. Other studies [30; 31] have used self-predicting modeling to ensure the effectiveness of feature learning. Inspired by computer vision processing methods, some scholars have converted TS data into image data and used image processing methods to extract features for TSC. Typical image transform methods include Gramian Angular Field (GAF) [8; 9], RP [10; 11], Markov Transition Fields (MTF) [12], and so on. However, these methods only use a single kind of feature. **Ensemble methods. ** This kind of method combines several effective methods, in order to obtain the most appropriate classification results from the classification results obtained through different mechanisms. It includes three typical methods: Elastic Ensemble (EE) [32], the collection of transformation ensembles (Flat-COTE) [33], and HIVE-COTE [4]. Among them, EE integrates 13 classification methods based on distance measurement; Flat-COTE includes 35 classification methods, where several feature-based classification methods are added in Flat-COTE; while HIVE-COTE is based on Flat-COTE, improving the mechanism of getting the final classification result from each sub-classification result. Time Series Combination of Heterogeneous and Integrated Embedding Forest (TS-CHIEF) [34] rivals HIVE-COTE in accuracy but is faster than HIVE-COTE. Random Convolutional Kernel Transform (ROCKET) [35] claims to achieve optimal performance with less computational cost. At the cost of a slight decrease in accuracy, Canonical Time-series Characteristics (Catch22) [36] generates a diverse and interpretable feature set with a greatly reduced number according to the properties of the TS data. The basis of these methods is still distance-based or feature-based; although the drawbacks of both types of methods are alleviated by ensembling, their flaws still exist.