time_gradual_change_check

What problem does it solve?

This Skill detects anomalies in time series data by identifying sudden jumps and oscillations in the data, helping users maintain the quality and consistency of their time series data.

Core Features & Use Cases

• Sudden Jump Detection: Identifies points where the change in data exceeds a specified threshold.
• Oscillation Detection: Detects patterns of repeated rise and fall within a specified window.
• Use Case: For instance, when monitoring sensor data, this Skill can alert you to sudden spikes or oscillations that may indicate a malfunction or other issue.

Quick Start

Run the time_gradual_change_check skill with the following command:

python scripts/time_gradual_change_check.py \
    --input_path /path/to/input.csv \
    --output_path /path/to/output.csv \
    --check_field temperature \
    --time_field timestamp \
    --time_format "%Y-%m-%d %H:%M:%S" \
    --jump_threshold 10.0 \
    --qc_mark QC0017 \
    --mark_field_name QC0000