###### Abstract  
The Key Laboratory of Integrated Microsystems (IMS) of Peking University Shenzhen Graduate School has deployed a self-developed acoustic and electromagnetics to artificial intelligence (AETA) system on a large scale and at a high density in China to comprehensively monitor and collect the precursor anomaly signals that occur before earthquakes for seismic prediction. This paper constructs several classic time series and non-time series prediction models for comparison and analysis in order to find the most suitable earthquake-prediction model among these models. The long short-term memory (LSTM) neural network, which gains the best results in earthquake prediction based on AETA data extracted from the precursor anomaly signals, is selected for real-earthquake prediction for 16 consecutive weeks. earthquake prediction; AETA; time series model; non-time series models; LSTM +
Footnote †: journal: Applied sciences  
LightGBM to analyze the seismic stability of a hypothetical embankment in 2021 [20]. [PERSON] constructed a random forest (RF) model to detect the location of an upcoming earthquake in 2022 [21]. The non-time series models developed in this paper include neural network (NN), SVM, gradient boosting decision tree (GBDT), RF and LightBGM models represented by integrated trees. Some experts use time series models for earthquake forecasting. Compared with non-time series models, time series models can preserve the richness of the data, and can learn the asymptotic changes of various features in the time scale, which can detect the earthquake precursor anomaly signals. [PERSON] et al. introduced a signal processing algorithm, which combined wavelets, neural networks and the Hilbert transform to predict earthquake activity in 2017 [22]. [PERSON], [PERSON] et al. combined convolutional and recurrent neural networks to pick phases from archived continuous waveforms in 2019 [23]. In 2019, [PERSON] et al. also used the gated recurrent unit (GRU) model to exploit temporal and frequency information from continuous seismic data to detect and classify continuous sequences of volcanic seismic events at the Deception Island Volcano, Antarctica [24]. In 2020, [PERSON] et al. developed a recurrent neural network (RNN) model to create an earthquake probability map for the eastern region of India, including the coastal state of Odisha [25]. [PERSON], [PERSON] et al. proposes a framework based on a long short-term memory (LSTM) neural network architecture for real-time regional seismic damage assessment in 2021 [26]. In 2021, [PERSON], [PERSON] et al. utilized two LSTM models to simulate and forecast hydrological variations based on hydrological time series of data from a monitoring site to identify possible precursors to the Lijiang earthquake [27]. In 2021, [PERSON], [PERSON] et al. introduced a moving-steps strategy and established three recurrent neural network models: simple-RNN, LSTM, and GRU models to the prediction of the slope dynamic response [28]. The main time series models established in this paper include the LSTM prediction model, the GRU, and the CNN+GRU stacking prediction model. [PERSON], [PERSON] et al. applied deep learning to extract SESs and develop a novel deep learning network based on geoelectric field characteristics by combining the LSTM in 2022 [29]. This paper analyzes and compares various non-time series models and time series models in order to find a more suitable and simpler model for earthquake prediction based on AETA data. In Section 2, this paper introduces the AETA system developed by the IMS lab and the process of constructing the dataset. In Section 3, the construction of the non-time series and time series models are introduced respectively. Section 4 shows the results of each prediction model. In Section 5, the prediction results of non-time series models and time series models are compared and analyzed, and LSTM in the time series model gets the best results in earthquake prediction based on AETA data.