QC15_TimeChangeRange

Time series anomaly detection with maximum change range marks data points where consecutive value differences exceed a predefined threshold. It identifies abrupt fluctuations for quality control in metrics like temperature or pressure.

For time series anomaly detection, you can load CSV, TSV, Excel, and SPSS file formats. The Skill requires Python and the pandas library to process these inputs and identify abnormal fluctuations.

Yes, this time series quality control tool requires the pandas library. You need a Python environment with pandas installed to execute the data processing and anomaly detection scripts.

The best way to identify sudden data spikes in a sensor time series is threshold-based anomaly detection. It calculates differences between consecutive values and marks points exceeding a specified maximum change range.

Use maximum change range inspection for data quality control when you need to identify abrupt changes within a specified time window. It is ideal for flagging issues in time-sensitive metrics like temperature or pressure.

To execute maximum change range inspection on a CSV file, run the Skill by specifying your input file path, setting an output path, and defining the threshold conditions for detecting abnormal data points.