## 2 Related Work  
From traditional methods to deep learning models, there are numerous techniques on forecasting financial time series, among which deep learning has received widespread attention due to its outperformance. Figure 1: An example showing that CNN can extract features of different time scales. **(a)** The daily closing price series learned by a convolutional neural network; **(b)** Description of the time scale of the features. LSTM is the most preferred deep learning model in studies of predicting financial time series. LSTM can extract dependencies in time series by the internal memory mechanism. In [11], LSTM networks were used for predicting out-of-sample directional movements for the constituent stocks of the S&P 500. They found LSTM networks outperform memory-free classification methods. [PERSON] et al. [12] constructed a trading model for the Chinese futures market through DRL and LSTM. They used deep neural networks to discover market features. Then an LSTM was applied to make continuous trading decisions. [PERSON] et al. [13] use Wavelet Transforms and Stacked Autoencoders to learn useful information in technical indicators and use LSTMs to learn time dependencies for the forecasting of stock prices. In [14], limit order book and history information was input to the LSTM model for the determination of the stock price movements. [PERSON] et al. [15] utilized limit order book and the LSTM model for the trend prediction. These works prove that LSTMs can successfully extract time dependencies in the financial sequence. However, these works do not consider the multiple time scale features in the price series. Several studies focused on utilizing CNN models inspired by their remarkable achievements in other fields, such as image recognition [26], speech processing [27], natural language processing [28], etc. Convolutional neural networks can directly extract features of the input without sophisticated preprocessing, and can efficiently process various complex data. [PERSON] et al. [16] used one-dimensional CNN with an agent-based RL algorithm to study Taiwan stock index futures. In [17], [PERSON] et al. convert price sequences into pictures and then use the CNN to learn useful features for prediction. In [18], a new CNN model was proposed to predict the trend of the stock prices. Correlations between instances and features are utilized to order the features before they are presented as inputs to the CNN. These works use CNNs to extract features of a single time scale in the price series. But in financial time series, multiple time scale features are ubiquitous, and it is meaningful to study them. Besides, there are some studies combine the advantages of CNN and LSTM to form hybrid networks. In [19], the proposed model makes the stock selection strategy by using the CNN and then makes the timing strategy by using the LSTM. [PERSON] et al. [20] proposed a Deep Co-investment Network Learning (DeepCNL) model, which combined convolutional and recurrent neural layers. Both [19; 20] take advantage of the combination of CNN and LSTM. However, they ignore the multiple time scale features that exist in the financial time series. In [21], numerous pipelines combining CNN and bi-directional LSTM for improved stock market index prediction. In [22], both convolutional and recurrent neurons are integrated to build the multi-filter structure, so that the information from different feature spaces and market views can be obtained. Although the authors in [21; 22] proposed models based on multi-scale features, they used multiple pipelines or networks to extract multi-scale features, which makes the model complex and vast, which is not conducive to training or obtaining useful information. Differing from previous work, we propose a hybrid neural network that mainly focuses on multiple time scale features in financial time series for trend prediction. We innovatively use a CNN to extract features on multiple time scales, simplifying the model and facilitating better predictions.