## 3 Dataset and Metrics  
### Dataset  
The journal metric time series dataset contains 5425 journals, and each journal corresponds to a time series consisting of five consecutive years of metric records for the same journal, with each metric record consisting of 21 categories of elements. To ensure diversity of the data sources in the dataset, metric records of journals for five consecutive years were collected from a total of 152 subdivisions, with 12 broad subject categories. Data collection, cleaning, and collation. As shown in Figure 1, we collected the contents of the dataset from a total of 152 subdivisions (e.g., the medical field, the engineering field, etc. ), with 12 disciplinary categories. The proportion of journals belonging to each of the 12 disciplinary categories in the dataset is shown in Figure 2. The dataset contains 5425 journals, each journal corresponds to a time series with five timesteps, and the contents comprise 21 types of metric records for each journal, for a total of 5 years, from 2017 to 2021. We first collected a large number of journal metric records by year from journal platforms such as WanFang and ZhiWang and used Excel records; then, we filtered the journals containing five consecutive years of records based on journal names and unique identifiers, stitched them according to journal names and unique identifiers, and sorted them according to journal names and years of records. Finally, we performed cleaning operations such as missing value filling and text transformation on the stitched data: For missing values, we used the more common mean-fill method, i.e., we used the mean valueof the category metric for the rest of the year of a journal, or directly filled it with 0 if all five years were missing; for text, we converted this into numerical elements as model inputs. The result of these steps is the time series dataset of journal metrics presented in this paper. Metric record categories. Each journal metric record contains three categories of journal overview attributes (journal name, journal subject classification, and metric record year) and 18 categories of journal evaluation metrics (regional distribution, grant ratio, the average number of authors, average number of citations, citation half-life, extended H metric, extended other citation rates, extended immediate-year metric, extended disciplinary impact metric, extended disciplinary diffusion metric, extended number of cited journals, extended total citation frequency, extended citation half-life, literatu  
Figure 1: Bar of classes of subject statistics for the time series dataset of journal metrics. Figure 2: Pie chart of the proportion of journals belonging to each of the 12 disciplinary categories in the journal metric time series dataset. of institutional distribution, volume of source literature, overseas paper ratio, and extended impact factor). Among them, the overview attributes are used to distinguish between the data for detailed analyses in this study; the journal evaluation metrics are used as core time series data for training and testing the models constructed by various time series analysis methods. Time series characteristics. Multiple multivariate, short-time series are included in the journal metrics time series dataset. Each journal in this dataset contains metric records for each of the last five years and thus can be viewed as a sample of 5425 time series of 5 time-step 18-dimensional variables, which can support experiments using multiple time series prediction methods. Since the variables in this dataset are evaluation metrics for journals by year, it is difficult to show obvious seasonality as a time series, and its multiple time series and multiple covariates make certain demands on the mining learning ability and association learning ability of the modeling algorithm. Therefore, there are some difficulties in the research of modeling algorithms for this dataset. Normalization.