## 2 Literature Review
### Time-Series Prediction Based on Neural Networks  
Because neural networks can automatically extract data features and have strong generalization abilities, they are widely used in the field of time-series prediction. At present, the mainstream neural network prediction methods can be divided into three categories in terms of their structure: the artificial neural network (ANN), convolutional neural network (CNN), and recurrent neural network (RNN), as well as a variant thereof: the long short-term memory network (LSTM).  
ANN-based models are usually composed of input neurons, hidden neurons, and output neurons. [PERSON] et al. used traditional techniques for trend reduction and seasonalization, and they used the results to train ANNs. The model was able to capture the nonlinear properties of complex time series and improve the prediction accuracy [31]. [PERSON] and [PERSON] used an ANN to predict the location and type of fault in faulty cables in a distribution system, and the prediction accuracy on a 20 kV distribution line dataset was 98% [32]. Compared with ANNs, CNNs have a stronger ability to extract relevant features due to the introduction of convolutional layers, which can effectively process multivariate time series [33]. [PERSON] et al. proposed a pre-training model based on a CNN to classify time series. The model used a convolution filter to capture time features on multiple time scales and classified and predicted datasets that included electrical equipment and sensors [34]. [PERSON] et al. proposed a convolutional neural network based on an adaptive adversarial network (ADACNN) and applied it to intelligent fault identification in the bearing mode for electromechanical equipment. Compared with a general CNN, it achieved a 4% accuracy advantage [35].  
As a special neural network model, the recurrent structure of an RNN can capture the dependencies in time-series data. [PERSON] et al. proposed an RNN-based model to deal with multivariate time series with missing values. By using partial PSD system data at different time intervals and modeling them to capture the long-range time dependence, the prediction effect was improved [36]. [PERSON] and [PERSON] used an RNN to predict fault data in a ventilation machinery system and electromechanical heating system, respectively [37]. However, RNNs have problems such as gradient explosion and disappearance, and they can only remember short-term information when processing long sequences [38]. In order to solve the above problems, [PERSON] and [PERSON] proposed a variant--the long short-term memory network (LSTM)--based on an RNN [39]. [PERSON] et al. used Attention-LSTM, which was based on an attention mechanism, to predict the fault rate of mechanical equipment. Experiments on a bearing fault dataset showed that the model had high prediction accuracy [40]. However, due to the introduction of more parameters, LSTM has a problem where the network size is difficult to determine in practical applications [41]. [PERSON] et al. used the PSO algorithm to optimize the LSTM parameters, solved the problem of the optimal scale of the LSTM model being difficult to determine, and predicted oil and gas faults in power grid transformer equipment [42].