Time series decomposition
AssigneeAlibaba Group Holding Limited
InventorsWen Qingsong, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Wotao Yin, Tao Yao
Life of this patent
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Abstract
Time series decomposition method and system are provided. Time series data including at least a seasonality component is received. A noise removal filter may be applied to the time series data. Furthermore, a trend component and the seasonality component are extracted from the time series data. A residual component may subsequently be extracted from the time series data based on the trend component and the seasonality component. Anomaly detection may then be performed on the trend component or the residual component to determine whether an anomaly exists in the time series data.
Claims17
A method implemented by one or more computing devices, the method comprising:
- receiving time series data comprising at least a seasonality component;
- applying a noise removal filter to the time series data;
- extracting a trend component from the time series data, wherein extracting the trend component from the time series data comprises:
- modeling the trend component in terms of at least a circulant matrix;
- and
- computing the modeled trend component using a predefined algorithm with a fast Fourier transform;
- extracting the seasonality component from the time series data;
- obtaining a residual component from the time series data based on the trend component and the seasonality component;
- and
- performing an anomaly detection on the trend component or the residual component to determine whether an anomaly exists in the time series data.
One or more computer readable media storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:
- receiving time series data comprising at least a seasonality component;
- applying a noise removal filter to the time series data;
- extracting a trend component from the time series data, wherein extracting the trend component from the time series data comprises:
- modeling the trend component in terms of at least a circulant matrix;
- and
- computing the modeled trend component using a predefined algorithm with a fast Fourier transform;
- extracting the seasonality component from the time series data;
- obtaining a residual component from the time series data based on the trend component and the seasonality component;
- and
- performing an anomaly detection on the trend component or the residual component to determine whether an anomaly exists in the time series data.
A system comprising:
- one or more processors;
- and
- memory storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:
- receiving time series data comprising at least a seasonality component;
- applying a noise removal filter to the time series data;
- extracting a trend component from the time series data, wherein extracting the trend component from the time series data comprises:
- modeling the trend component in terms of at least a circulant matrix;
- and
- computing the modeled trend component using a predefined algorithm with a fast Fourier transform;
- extracting the seasonality component from the time series data;
- obtaining a residual component from the time series data based on the trend component and the seasonality component;
- and
- performing an anomaly detection on the trend component or the residual component to determine whether an anomaly exists in the time series data.
Dependencies are inferred from the claim text.