Methods and systems for detecting anomalies include determining a predictive model for each pair of a set of time series, each time series being associated with a component of a system. New values of each pair of time series are compared to values predicted by the respective predictive model to determine if the respective predictive model is broken. A number of broken predictive models is determined. An anomaly alert is generated if the number of broken predictive models exceeds a threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for detecting anomalies, comprising: determining a predictive model for each pair of a set of time series, each time series being associated with a component of a system; comparing new values of each pair of time series to values predicted by the respective predictive model to determine if the respective predictive model is broken; determining a number of broken predictive models; and generating an anomaly alert if the number of broken predictive models exceeds a threshold. 2. The method of claim 1, wherein determining the predictive model for a pair of time series comprises determining a periodic relationship between the time series and a non-periodic relationship between the time series. 3. The method of claim 2, wherein determining the predictive model for a pair of time series further comprises performing a Fourier transform on the time series to separate periodic components from non-periodic components. 4. The method of claim 2, wherein determining the predictive model for a pair of time series further comprises determining a time delay between time series that have a periodic relationship. 5. The method of claim 2, further wherein determining a non-periodic relationship between the time series comprises determining polynomial bases for modeling a polynomial relationship between the time series. 6. The method of claim 5, wherein determining a non-periodic relationship further comprises solving a corresponding Sparse Group Lasso problem and correcting coefficients of a solution of the corresponding Sparse Group Lasso problem by linear regression. 7. The method of claim 1, wherein comparing values of each pair of time series only uses predictive models that have a fitness score higher than a threshold. 8. The method of claim 7, further comprising calculating a fitness score for each predictive model based on an r-squared error. 9. A method for detecting anomalies, comprising: determining a predictive model for each pair of a set of time series, each time series being associated with a component of a system, by determining a periodic relationship between the time series and a non-periodic relationship between the time series; comparing new values of pairs of time series having predictive models that have a fitness score higher than a threshold to values predicted by the respective predictive model to determine if the respective predictive model is broken; determining a number of broken predictive models; and generating an anomaly alert if the number of broken predictive models exceeds a threshold. 10. The method of claim 9, wherein determining the predictive model for a pair of time series further comprises performing a Fourier transform on the time series to separate periodic components from non-periodic components. 11. The method of claim 9, wherein determining the predictive model for a pair of time series further comprises determining a time delay between time series that have a periodic relationship. 12. The method of claim 9, wherein determining a non-periodic relationship between the time series comprises determining polynomial bases for modeling a polynomial relationship between the time series. 13. A system for detecting anomalies, comprising: a fitting module configured to determine a predictive model for each pair of a set of time series, each time series being associated with a component of a system; an anomaly detection module configured to compare new values of each pair of time series to values predicted by the respective predictive model to determine if the respective predictive model is broken and to determine a number of broken predictive models; and an alert module configured to generate an anomaly alert if the number of broken predictive models exceeds a threshold. 14. The system of claim 13, wherein the fitting module is further configured to determine a periodic relationship between the time series and a non-periodic relationship between the time series. 15. The system of claim 13, wherein the fitting module is further configured to perform a Fourier transform on the time series to separate periodic components from non-periodic components. 16. The system of claim 13, wherein the fitting module is further configured to determine a time delay between time series that have a periodic relationship. 17. The system of claim 13, wherein the fitting module is further configured to polynomial bases for modeling a polynomial relationship between the time series. 18. The system of claim 17, wherein the fitting module is further configured to solve a corresponding Sparse Group Lasso problem and correcting coefficients of a solution of the corresponding Sparse Group Lasso problem by linear regression. 19. The system of claim 13, wherein the anomaly detection module is further configured to use only predictive models that have a fitness score higher than a threshold. 20. The system of claim 19, wherein the fitting module is further configured to calculate a fitness score for each predictive model based on an r-squared error.