# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 1 Introduction  
Time-series (TS) data is a set of values sequentially ordered in time, which is seen frequently in real-life, such as financial data, trajectory data, weather data, and so on. With the development and application of the Internet of Things (IoT), the data collected by various sensors is also TS data. Research on TS data is diverse, such as compression, storage and query, anomaly detection, prediction, and so on [1]. This paper focuses on the time-series classification (TSC) task, the purpose of which is to classify concrete TS data into pre-determined categories which have similar characteristics. For the task of TSC, our predecessors have done a lot of research and produced many methods. These methods are mainly based on distance functions, such as dynamic time warping (DTW) [2]; features, such as the shapelet transform (ST) [3]; and ensemble methods, such as the hierarchical vote COTE (HIVE-COTE) [4], combining the former two types of methods. With the development and application of deep learning, a large number of methods for this task have also been proposed. Researchers typically use various transformation methods or artificially constructed features to input into deep neural networks (DNN) and train a model for TSC; yet, some methods directly use DNN, such as long short-term memory (LSTM) [5], to process raw TS data and learn an end-to-end classification model. Human beings can use visual observation to distinguish the differences between TS records, according to key features, and classify them. Visualization is also very important in scientific research. In the field of natural language processing (NLP), Chinese character representations have been improved by using the pictographic meanings of Chinese characters [6]. In the field of TS research, features extracted by visualization have been used for the task of prediction [7]. Among deep learning methods for TSC, a type of method converts TS data into image data, then use some image processing methods to classify the TS data. Representative transformation methods include Gramian fields [8; 9], recurrence plots (RP) [10; 11], Markov transition fields (MTF) [12], and so on. Usually, a human's perception of things is a combination of human senses from different aspects, while the deep learning methods for TSC proposed so far have either used only sequential features or visualization features. In the field of few-shot learning, scholars have used DNNs to extract data features from different perspectives, in order to improve the performance of deep learning models on small training data sets [13; 14]. Enlightened by these advances, we propose a framework, TSC-FF, based on deep learning to learn features from different aspects, which enhances the feature space and replaces complex, artificially constructed features, in order to remove the bias due to manually designed features, as well as to avoid the limitations of domain knowledge. Specifically, in order to enable the network to learn the most discriminant useful features for the classification task, we use well-trained LSTM-A neural networks and CNN-A neural networks to extract the features of TS data. LSTM-A is used to extract sequence features, while CNN-A is used to extract visualization features after the TS data is visualized. Our approach differs from the existing visual transformation methods, such as Gramian fields [8; 9], RP [10; 11], and MTF [12]. In order to imitate human vision (i.e., what we see is what we get), we take the most direct visualization method, transforming the TS data into an Area Graph. Area Graphs are a type of Line Graph, but with the area below the line filled in with a certain colour or texture. Compared with the existing visualization methods, Area Graph is simpler and avoids the loss of some information in complex conversions and additional computational cost. In addition, an attention mechanism is used to enhance the features of key points, which is generated from contributing regions in the data for the specific labels.