# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 2 Background Knowledge
### Recurring Event Prediction  
Recurring events refer to those events that occur or appear on a regular basis, but the causes may be different. For example, multiple types of protests and violent events in countries with a large number of Muslim people usually take place after the communual prayer on Fridays since there are crowds gathered [17]. Such types of events can be predicted using frequent pattern mining-based methods, time series forecasting-based methods, and temporal point process-based methods.  
The frequent pattern mining-based methods predict certain events by exploring the frequent occurrence patterns from a series of historical events. As the type of events to be predicted is determined, the key to this method is to search the associated event sets that frequently co-occur in the previous event streams. An integrated framework was designed to discover the frequent episodes, which were composed by the continuous event sequences, to predict a specific type of events [18; 19] or multiple types of events [20]. [PERSON], et al. [21] further sorted the predicted events by confidence in a descending order to retrieve the top-k events so as to improve the reliability of prediction results.  
The time series forecasting-based methods partition the event streams into a series of events based on constant time intervals, e.g., one hour, one day, or one week. [PERSON] [22] took the instability of events in time sequences into consideration and adopted the Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model to predict the levels of violence in the Afghanistan area. Given a sequence of terrorist incidents data with  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Event Type** & **Cause** & **Timing** \\ \hline Planned events & \(\surd\) & \(\surd\) \\ \hline Recurring events & \(\times\) & \(\surd\) \\ \hline Spontaneous events & \(\surd\) & \(\times\) \\ \hline Black swan events & \(\times\) & \(\times\) \\ \hline \hline \end{tabular} Note: “\(\surd\)” means known and “\(\times\)” means unknown.  
\end{table}
Table 1: Four types of events with known or unknown timing and cause.  
different states, [PERSON], et al. [23] predicted the future event state by searching for the state that was most likely to generate the sequence data based on the Hidden Markov Model (HMM). A similar approach was also applied for communication system failure detection, showing effective and efficient performance [24]. Air pollution stands as one of the most detrimental environmental issues globally, necessitating the need for effective and accurate prediction of ozone concentrations. [PERSON], et al. [25] employed the seasonal trend decomposition method to decompose the time series into three distinct sub-series, i.e., long-term trend, seasonal trend, and random series, for ozone concentration prediction. The methodology of time series prediction is also commonly applied in the realm of studying infectious diseases. [PERSON] [26] proposed a time series prediction model using machine learning techniques to capture disease curves and forecast epidemic trends.  
Different from time series forecasting-based methods that predict events with equal time intervals, the temporal point process-based methods model the events with temporal heterogeneity in order to generate more accurate time stamps. The classical models such as the Poisson point process model [27; 28], [PERSON] point process model [29], and Weibull point process model [30] for event prediction purposes. As deep learning shows great potential for mining hidden patterns, some scholars have proposed the deep point process model by leveraging neural network technologies for event prediction but with less interpretability [31].