# A New Method of Time-Series Event Prediction Based on Sequence Labeling
## 3 Experiment
### Introduction to Data Sets and Prediction Tasks  
This article used five real-world data sets for experimental exploration. All five data sets are public data from Kaggle:  
1. DJIA 30 Stock Time Series;
2. Web Traffic Time Series Forecasting;
3. Air Quality Data in India (2015-2020);
4. Daily Climate time series data.  
Except for the WebTraffic data set, the rest are small sample data. Table 1 shows the relevant information of these data sets.  
**DJIA30**: This data set is a stock time-series. It contains the stock price data set of 30 DJIA companies in the past 13 years (518 weeks in total). It contains five trading days each week, and each trading day records four observations: opening price, trading volume, highest price, and lowest price reached on the day. If the slope of one of the four observations of a stock in a week is greater than 1, it is determined that the price of the week fluctuates abnormally. The prediction task is to predict whether there will be abnormal price fluctuations in the next week, based on the observed values in the past year (50 weeks).  
**WebTraffic(Web)**: This data set is a time-series of network traffic. It contains the number of views of 50,000 Wikipedia articles in the past 2 years (26 months in total), 30 days each month, and one observation per day is recorded. When the slope of the observed value curve of an article in a month is greater than 1.0, it is determined that the reading volume of the article has increased rapidly this month. The prediction task is to predict whether there will be a rapid increase in reading volume in the next month, based on the observations in the past year (12 months).  
**PM2.5**: This data set is a time-series of air quality. It contains the PM2.5 detection concentrations of 38 air detection stations in the past 3 years (130 weeks in total), 7 days a week, and one observation value is recorded every day. If the observed mean value of a testing station in a week is too large (two variances exceeding the overall mean value), it is determined that there is an abnormality in the PM2.5 concentration in that week. The prediction task is to predict whether there will be an abnormal change in concentration in the next week, based on the observed values in the past five months (20 weeks).  
**CO**: This data set is a time-series of air quality. It contains the CO detection concentrations of 24 Italian cities in the past 4 months (2664 h in total), 24 h a day, and an observation value is recorded every hour. If the difference between the maximum value and the minimum value observed in a city in a day is too large, it is determined that there is an abnormality in the CO concentration of that city on that day. The prediction task is to predict whether there will be an abnormal change in concentration in the next day, based on the observed values in the past three weeks (20 days).  
**Temperature(Temp)**: This data set is a temperature time-series. It contains the temperature of 100 representative cities in the world in the past 4 years (222 weeks in total), 7 days a week, and one observation value is recorded every day. If the difference between the maximum value and the minimum value observed in a city in a week is too large, it is determined that the temperature of the city on that day is abnormal. The prediction task is to predict whether there will be an abnormal temperature change in the next week based on the observed value in the past year (50 weeks).