地球资源数据云——数据资源详情
教育时间序列数据
该数据集《Educational Time Series Data》主要用于二分类任务，数据形态以时序/信号为主。 题目说明：Ready - to - use features including trends, seasonality, and multiple targets 任务类型：时序/信号二分类。 建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：time - series - data.csv。 This dataset is a feature - engineered time series dataset created from the Tutorial: Tutorial - TSA - EDA - Time Series Data notebook. It includes a wide range of engineered temporal, rolling, statistical, and lag - based features suitable for time - series forecasting, anomaly detection, and exploratory data analysis. The dataset contains: Original target variable transformations (lags, differences, rolling statistics, exponential moving averages, etc.) Date - based features (year, month, day, day of year, weekend flags, leap year, season, etc.) Advanced statistical features (volatility, skewness, kurtosis, entropy, Sharpe ratio, drawdown)
摘要概览
该数据集《Educational Time Series Data》主要用于二分类任务，数据形态以时序/信号为主。 题目说明：Ready - to - use features including trends, seasonality, and multiple targets
任务类型：时序/信号二分类。
建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：time - series - data.csv。
This dataset is a feature - engineered time series dataset created from the Tutorial: Tutorial - TSA - EDA - Time Series Data notebook. It includes a wide range of engineered temporal, rolling, statistical, and lag - based features suitable for time - series forecasting, anomaly detection, and exploratory data analysis.
The dataset contains:
Original target variable transformations (lags, differences, rolling statistics, exponential moving averages, etc.)
Date - based features (year, month, day, day of year, weekend flags, leap year, season, etc.)
Advanced statistical features (volatility, skewness, kurtosis, entropy, Sharpe ratio, drawdown)