Device, method, and system for concept drift detection

A concept drift detection method for detecting concept drift in a time series data set, the concept drift detection method comprising:

• receiving a time series data set that includes a set of past time series data relating to a first time period and a set of current time series data relating to a second time period subsequent to the first time period;
• generating a baseline model based on a subset of the set of past time series data;
• dividing the set of past time series data into a set of past windows;
• dividing the set of current time series data into a set of current windows;
• and
• dividing a set of baseline data created by the baseline model into a set of baseline windows;
• calculating a set of baseline data features from the set of baseline windows;
• calculating a set of past data features from the set of past windows;
• calculating a set of current data features from the set of current windows;
• calculating a baseline distance between a subset of the set of past data features and a subset of the baseline data features that relate to a corresponding time frame;
• calculating a current distance between a subset of the set of current data features and a subset of the baseline data features that relate to a corresponding time frame;
• calculating, based on the baseline distance, a baseline statistic that indicates a reference for determining concept drift;
• and
• determining, based on the baseline statistic and the current distance, presence or absence of concept drift between the set of current time series data and the set of past time series data.

A concept drift detection system for detecting concept drift in a time series data set, the concept drift detection system comprising:

• a client device;
• a data storage device configured to store a time series data set that includes a set of past time series data relating to a first time period and a set of current time series data relating to a second time period subsequent to the first time period;
• and
• a concept drift detection device configured to detect concept drift in the time series data set,
• wherein the concept drift detection device further includes:
• a data input unit configured to receive the time series data set from the data storage device;
• a baseline model generation unit configured to generate a baseline model based on a subset of the set of past time series data;
• a feature extraction unit configured to:
• divide the set of past time series data into a set of past windows, divide the set of current time series data into a set of current windows, and divide a set of baseline data created by the baseline model into a set of baseline windows, and
• calculate a set of baseline data features from the set of baseline windows, calculate a set of past data features from the set of past windows, and calculate a set of current data features from the set of current windows;
• a distance calculation unit configured to:
• calculate a baseline distance between a subset of the set of past data features and a subset of the baseline data features that relate to a corresponding time frame, and
• calculate a current distance between a subset of the set of current data features and a subset of the baseline data features that relate to a corresponding time frame;
• and
• a concept drift detection unit configured to:
• calculate, based on the baseline distance, a baseline statistic that indicates a reference for determining concept drift, and
• determine, based on the baseline statistic and the current distance, presence or absence of concept drift between the set of current time series data and the set of past time series data, and output a concept drift notification to the client device in a case that concept drift is determined to be present between the set of current time series data and the set of past time series data.