###### Abstract  
In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders' trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.  
stock market index trend prediction; multiple time scale features; deep learning; convolutional neural network; long short-term memory neural network +
Footnote †: journal: Medical  
can reflect the short-term fluctuation of the price. The combination of multi-scale features facilitates accurate prediction.  
In recent years, the convolutional neural network (CNN) has shown high power in feature extraction. Inspired by existing research, we use a CNN to extract multiple time scale features for more comprehensive learning of price sequences. For instance, the daily closing price series in Figure 1a is learned by a two-layer convolutional neural network. The outputs of the two layers of the CNN are called Feature map1 and Feature map2, respectively. As illustrated in Figure 1b, each point of the feature map corresponds to a region of the original price series (termed the receptive field [23]), and it can be considered as the description for the region. Due to different receptive fields, Feature map1 and Feature map2 describe the input price series from two-time scales. Compared with Feature map1, Feature map2 describes the original price sequence on a larger time scale. Therefore, we regard the outputs of different layers of the CNN as features of varying time scales of the original price series.  
Meanwhile, the Long Short-Term Memory (LSTM) network works well on sequence data with long-term dependencies due to the internal memory mechanism [24; 25]. Many studies use LSTM networks to learn the long-term relationship of features extracted by the CNN. In this way, we utilize LSTMs to learn long-term dependencies of multiple time scale feature sequences obtained by the CNN.  
In this paper, we present a novel end-to-end hybrid neural network to learn multiple time scale features for predicting the trend of the stock market index. The network first combines the features obtained from different layers of the CNN with daily price subsequences to form multiple time scale features, reflecting the short-, medium-, and long-term laws of price series. Subsequently, three LSTM recurrent neural networks are utilized to capture time dependencies in multiple time scale features obtained in the previous step. Then, several fully connected layers combine features learned by LSTMs to predict the trend of the stock market index. The experimental analysis of real datasets demonstrates that the proposed hybrid network outperforms a variety of baselines in terms of trend prediction in accuracy.  
The rest of the paper is organized as follows. Section 2 presents related work. In Section 3, we present the proposed hybrid neural network based on multiple time scale features of the stock market index. Section 4 reports experiments and results, and the paper is discussed in Section 5.