Data Evaluation

Before using data for forecasting or model training, it is important to understand whether the data quality is sufficient. Data evaluation scores data from two angles: integrity and forecastability, and then calculates an overall score. Higher scores indicate better data quality and better suitability for modeling and forecasting analysis.

Start Evaluation

On the Data Evaluation page, select the dataset to evaluate and click Start Evaluation.

After the evaluation is complete, the page shows the integrity, forecastability, and overall score, and keeps the result in the evaluation records. Click View to check the detailed results.

Three Evaluation Results

Integrity

This score tells you whether the data has missing values and whether key fields are complete.

Sensor outages, network jitter, duplicate reports, missing fields, and similar issues can introduce gaps, duplicates, or incomplete structures in time-series data. The integrity score reflects how severe these issues are.

Score	Meaning	Suggestion
80–100	Data integrity is good, key fields are complete, and modeling requirements are met	Safe to use directly
60–79	A small amount of data is missing, with limited impact on analysis results	Use after data governance
0–59	A large amount of data is missing, which may affect model training results	Prioritize data governance

If the integrity score is low, forecasting or analysis results may be affected. Consider handling missing values, duplicate data, abnormal timestamps, or incomplete key fields before continuing with modeling.

Forecastability

This score tells you whether the series has learnable patterns.

Some data is naturally regular, such as electricity usage at fixed times of day or temperature that changes with seasons. Other data behaves more like random fluctuations. The forecastability score reflects how strong the patterns in the series are.

Score	Meaning	Suggestion
80–100	Time-series features are stable and suitable for building forecasting models	Good candidate for modeling
60–79	The data has some regularity and can be used for forecasting analysis, but model performance may be affected by volatility	Try modeling and interpret results with business context
0–59	The data fluctuates significantly and is difficult to forecast	Review the data or forecasting strategy

If the forecastability score is low, it does not necessarily mean the data is wrong. It may simply mean the series is highly volatile and hard to forecast. Use business context to decide whether to introduce additional variables or lower expectations for forecasting accuracy.

Overall Score

This score tells you whether the dataset is generally suitable for modeling and forecasting analysis.

The overall score combines integrity and forecastability to provide a general data quality judgment. It can be used to quickly decide whether data is ready for the next step, and to help prioritize datasets that need governance.

Score	Meaning	Suggestion
80–100	Data quality is good and suitable for modeling and forecasting analysis	Use with priority
60–79	Data quality basically meets requirements, and better results can be achieved after governance	Improve based on the weak dimension
0–59	The data does not meet high-quality modeling requirements, and forecasting results may be unstable	Complete governance before modeling

If the overall score is low, the data usually has clear weaknesses in integrity or forecastability. Check the detailed results first to locate the specific issues, then perform data governance.

Quick Reference

Metric	Key Question	Low Score Indicates
Integrity	Is the data complete? Are key fields complete? Are there missing values, duplicates, or abnormal timestamps?	Missing data, duplicates, time-ordering issues, or incomplete fields
Forecastability	Does the series have cyclic, trend, or stable patterns?	The series is close to random fluctuations and is hard to extrapolate from history
Overall Score	Is the data generally suitable for modeling and forecasting analysis?	Data quality has weaknesses and should be improved before use