# A New Method of Time-Series Event Prediction Based on Sequence Labeling
###### Abstract  
In the existing research on time-series event prediction (TSEP) methods, most of the work is focused on improving the algorithm for classifying subsequence sets (sets composed of multiple adjacent subsequences). However, these prediction methods ignore the timing dependence between the subsequence sets, nor do they capture the mutual transition relationship between events, the prediction effect on a small sample data set is very poor. Meanwhile, the sequence labeling problem is one of the common problems in natural language processing and image segmentation. To solve this problem, this paper proposed a new framework for time-series event prediction, which transforms the event prediction problem into a labeling problem, to better capture the timing relationship between the subsequence sets. Specifically, the framework used a sequence clustering algorithm for the first time to identify representative patterns in the time series, then represented the set of subsequences as a weighted combination of patterns, and used the eXtreme gradient boosting algorithm (XGBoost) for feature selection. After that, the selected pattern feature was used as the input of the long-term short-term memory model (LSTM) to obtain the preliminary prediction value. Furthermore, the fully-linked conditional random field (CRF) was used to smooth and refine the preliminary prediction value to obtain the final prediction result. Finally, the experimental results of event prediction on five real data sets show that the CX-LC method has a certain improvement in prediction accuracy compared with the other six models. TSEP; sequence labeling; pattern recognition; XGBoost; LSTM; CRF +
Footnote †: journal: Medical Image Processing  
models such as ARIMA, SARIMA and GARCH. The main methods of event detection include decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM). However, since time series events usually span a certain time range, these traditional forecasting models predict a single value and then classify it, are susceptible to small disturbances in the training data, and have a high degree of instability [8]. This type of method ignores the characteristics of each subsequence itself, and its predictive ability is limited. In recent years, the new event prediction method represented by the graph model Time2 Graph [9] has received widespread attention. Based on Time2 Graph, the EvoNet model [10] has more successfully captured the time-varying relationship between time series. The specific steps are: representing and identifying representative patterns of subsequences, capturing the transitional relationship between the patterns, using pattern features as the input of the classifier and the classification result as the prediction of future events, so as to predict the event. The key to this prediction method is how to extract the characteristics of time subsequences. The above two methods extract the characteristics by establishing state diagrams. In addition, the time series can also be interpreted as a new state sequence and model their sequential dependencies. The literature provides a new method, X-HMM (XGBoost-HMM) [11], which takes the hidden state as the input of the classifier. Compared with the traditional framework, this kind of method omits the step of time series forecasting, thus avoiding the deviation caused by time series forecasting, but this method classifies each subsequence set separately, ignoring the remote dependencies between the various subsequence sets. Inspired by the sequence labeling method, the use of depth models and probabilistic graph models for sequence labeling can well capture the dependencies between subsequence sets [12] and transfer the idea of sequence labeling to event prediction, which can greatly improve forecasting accuracy. As a classic sequence data processing model, LSTM has a solid theoretical foundation [13].