# A New Method of Time-Series Event Prediction Based on Sequence Labeling
## 4 Conclusions and Future Work  
Based on the idea of sequence labeling, this paper proposed a novel time-series event prediction framework, namely cluster-XGBoost-LSTM-CRF. This model captured the pattern information of subsequences through pattern recognition, and the state of the pattern is the key to determining the occurrence of an event. The labeling prediction of LSTM can capture the interaction between the set of subsequences and the long-term dependencies between events. In order to verify the effectiveness of CX-LC, we conducted extensive experiments on five real-world data sets. Experimental results show that our model is better than other popular benchmarking methods. Based on this, the time-series event prediction problem was transformed into a labeling problem, which can complete the prediction task well. In the five data sets, the CX-LC model has a good prediction effect on the change trend of temperature and air quality, indicating that the model is more suitable for data sets with less human intervention and is relatively stable, so it can be used to provide a relevant basis for atmospheric environmental impact assessment, planning, management, and decision-making. However, for more complex and larger data sets, the prediction effect of such models is poor. How to capture more complex time series patterns and how to solve the problem of uneven samples still needs to be studied. The CX-LC model is only a preliminary exploration of applying the idea of sequence labeling to the problem of event prediction. The model still has shortcomings, and there are directions that can continue to be explored:  
1. When performing feature extraction of subsequence sets, we can consider the pattern changes between subsequences, construct the pattern evolution diagram, and enrich the feature information;
2. The LSTM model is a relatively simple and conventional sequence labeling algorithm; only the sequence structure is considered in the calculation process, if the pattern is successfully constructed for the evolution diagram, we can try a more comprehensive network structure, such as GNN, GCN, etc. ;
3. In the event prediction, the periodicity and seasonality of the original data were not considered, although such characteristics can be captured when fitting the CX-LC model. However, if these effects are directly reflected in the structure of the model, it may be able to improve the forecasting progress of the model;
4. The results of 4.4 show that the performance of CX-LC on small-sample data sets is better than other models, but the prediction effect on large data sets still needs to be improved. There is still a lot of work to be carried out on how to improve the framework based on sequence annotation to be more suitable for event prediction. In the future, we will also look for better event prediction methods for different types of time data sets. Conceptualization, S.L. and Z.Z. ; methodology, S.L. ; validation, S.L., K.S. and Z.Z. ; formal analysis, S.L. ; investigation, Z.Z. ; resources, S.L. and K.S. ; writing--original draft preparation, Z.Z. ; writing--review and editing S.L. and K.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Some data presented in this study are available in [10]. The authors declare no conflict of interest. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances. _Data Min. Knowl. Discov._**2017**, _31_, 606-660. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2012) [PERSON]; [PERSON] Markov-switching autoregressive models for wind time series. _Environ. Model. Softw._**2012**, _30_, 92-101. [CrossRef]
* [PERSON] and [PERSON] (2014) [PERSON]; [PERSON] Hmm-based hybrid meta-clustering ensemble for temporal data. _Knowl.-Based Syst._**2014**, _56_, 299-310.