# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
## 1 Introduction  
With the advancement of today's social science and technology, the structure of data has become more complex. The amount of data has been ever-increasing, which marks our entry into the era of big data [1]. As data has become complex and massive, its hidden information, value, and laws have also increased. Therefore, only by fully mining and analyzing a large number of details in life can the knowledge and value contained in it be extracted and further fed back to people's daily production and life. The analysis of big data is usually divided into two parts. On the one hand, a summary conclusion is obtained through the study of big historical data in the past few months or years. The other kind of analysis is performed through the study of big historical data digging and predicting the state of work in the next few months. With the continuous improvement of social productivity and the increasing demand of people for future cognition, predicting the future state through the analysis of big historical data has become the top priority in the considerable current data analysis work [2]. The development of deep learning, especially neural networks, has shown strong performance and brought new vitality to extensive data analysis and prediction, and has achieved excellent results. Nowadays, deep learning, especially with neural networks,is widely used in extensive data analysis and forecasting in various industries, such as weather forecasting [3], traffic flow forecasting [4], financial forecasting [5], medical forecasting [6], etc. LSTM [7] networks have shown sound prediction effects and stood out among many neural networks. The network solves the problem of gradient disappearance and gradient explosion in the long sequence training process of the recurrent neural network (RNN) [8]. The network has certain advantages in sequence modeling and has a long-term memory function. At present, LSTM is widely used in big data prediction, machine translation, image processing, encoding/decoding, and other fields. With the advancement of technology, data collection has become more accessible, and multivariate time series data [9] came into being. This type of data often has the characteristics of massive data, close time points, and many related variables. The change of a sequence often depends on the changes of several or even dozens of sequences. Multivariate time series data often have multiple time-related variables, and each variable depends not only on its past value but also on other variables. These kinds of mutual influence rules between sequences allows us to grasp more information, capture the law of sequence changes, and improve prediction accuracy. The generation of multiple time series data has brought significant challenges to extensive data analysis and forecasting. A single neural network has not achieved a good prediction effect in the prediction of multiple time series data, and it also exposed some problems in the neural network. The generation of the attention mechanism [10] makes up for the shortcomings of a single neural network's insufficient processing capability for multiple time series data. The attention mechanism can assign necessary weights to different representations, ignoring noise and redundancy in the input. The attention mechanism can also directly evaluate which inputs are preferred for the next task by checking the weights. In short, the attention mechanism can remove redundant information and noise in the multivariate time series data, and filter the multivariate time-related variables, assign different weights, and then filter the input of the following neural network. The main contributions of our model are as follows:  
1. We propose a long short-term memory neural network (ATT-LSTM) based on the attention mechanism for multivariate time series data prediction. 2. Use an attention mechanism to process multiple time series data.