Techniques are described for classifying seasonal patterns in a time series. In an embodiment, a set of time series data is decomposed to generate a noise signal and a dense signal, where the noise signal includes a plurality of sparse features from the set of time series data and the dense signal includes a plurality of dense features from the set of time series data. A set of one or more sparse features from the noise signal is selected for retention. After selecting the sparse features, a modified set of time series data is generated by combining the set of one or more sparse features with a set of one or more dense features from the plurality of dense features. At least one seasonal pattern is identified from the modified set of time series data. A summary for the seasonal pattern may then be generated and stored.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: generating, for a set of time series data, a noise signal and a dense signal; wherein the noise signal includes a plurality of sparse features from the set of time series data; wherein the dense signal includes a plurality of dense features from the set of time series data; selecting, from the noise signal, a set of one or more sparse features to retain; generating a modified set of time series data by combining the set of one or more sparse features with a set of one or more dense features from the plurality of dense features; identifying, from the modified set of time series data, at least one seasonal pattern; generating and storing, in volatile or non-volatile storage, summary data that identifies the at least one seasonal pattern. 2. The method of claim 1, wherein the set of one or more sparse features that are selected does not include at least one sparse feature, of the plurality of sparse features, that does not periodically recur. 3. The method of claim 1, wherein the set of one or more sparse features that are selected does not include at least one sparse feature, of the plurality of sparse features, that are determined to be statistically insignificant. 4. The method of claim 1, wherein selecting, from the noise signal, a set of one or more sparse features to retain comprises; identifying a candidate set of sparse features that have a magnitude above a threshold value; removing one or more sparse features from the candidate set of sparse features by performing conditional averaging of the candidate set of sparse features; retaining sparse features from the candidate set of sparse features that have not been removed by the conditional averaging. 5. The method of claim 1, wherein the set of one or more dense features does not include at least one dense feature of the plurality of dense features. 6. The method of claim 1, wherein identifying, from the modified set of time series data, at least one seasonal pattern comprises selecting a first set of sub-periods from the set of modified time series data that represent seasonal highs and selecting a second set of sub-periods from the modified time series data that represent seasonal lows; wherein the summary data identifies the seasonal highs and the seasonal lows. 7. The method of claim 6, further comprising: determining, based on the noise signal, which of the seasonal highs represent sparse seasonal highs; and wherein the summary data identifies which of the seasonal highs represent sparse seasonal highs. 8. The method of claim 1, wherein identifying, from the modified set of time series data, at least one seasonal pattern comprises: generating for the at least one seasonal pattern, a bit-vector; wherein each respective bit in the bit-vector corresponds to a different respective instance of a season; wherein a bit value of the respective bit indicates whether the respective instance is associated with the seasonal pattern; generating, based on the bit-vector, at least one segment that identifies one or more stretches of time that represent the seasonal pattern. 9. The method of claim 1, wherein identifying, from the modified set of time series data, at least one seasonal pattern comprises applying a set of one or more classification function to the modified time series data; wherein each classification function in the set of one or more classification functions generates a respective classification that identifies which instances of a season are recurrent highs and which instances of the season are recurrent lows. 10. The method of claim 1, further comprising causing display of the summary data that identifies the at least one seasonal pattern. 11. A one or more non-transitory computer-readable media comprising: instructions for generating, for a set of time series data, a noise signal and a dense signal; wherein the noise signal includes a plurality of sparse features from the set of time series data; wherein the dense signal includes a plurality of dense features from the set of time series data; instructions for selecting, from the noise signal, a set of one or more sparse features to retain; instructions for generating a modified set of time series data by combining the set of one or more sparse features with a set of one or more dense features from the plurality of dense features; instructions for identifying, from the modified set of time series data, at least one seasonal pattern; instructions for generating and storing, in volatile or non-volatile storage, summary data that identifies the at least one seasonal pattern. 12. The one or more non-transitory computer-readable media of claim 11, wherein the set of one or more sparse features that are selected does not include at least one sparse feature, of the plurality of sparse features, that does not periodically recur. 13. The one or more non-transitory computer-readable media of claim 11, wherein the set of one or more sparse features that are selected does not include at least one sparse feature, of the plurality of sparse features, that are determined to be statistically insignificant. 14. The one or more non-transitory computer-readable media of claim 11, wherein instructions for selecting, from the noise signal, a set of one or more sparse features to retain comprise instructions for: identifying a candidate set of sparse features that have a magnitude above a threshold value; removing one or more sparse features from the candidate set of sparse features by performing conditional averaging of the candidate set of sparse features; retaining sparse features from the candidate set of sparse features that have not been removed by the conditional averaging. 15. The one or more non-transitory computer-readable media of claim 11, wherein the set of one or more dense features does not include at least one dense feature of the plurality of dense features. 16. The one or more non-transitory computer-readable media of claim 11, wherein instructions for identifying, from the modified set of time series data, at least one seasonal pattern comprise instructions for selecting a first set of sub-periods from the set of modified time series data that represent seasonal highs and selecting a second set of sub-periods from the modified time series data that represent seasonal lows; wherein the summary data identifies the seasonal highs and the seasonal lows. 17. The one or more non-transitory computer-readable media of claim 16, wherein the instructions further include: instructions for determining, based on the noise signal, which of the seasonal highs represent sparse seasonal highs; and wherein the summary data identifies which of the seasonal highs represent sparse seasonal highs. 18. The one or more non-transitory computer-readable media of claim 11, wherein instructions for identifying, from the modified set of time series data, at least one seasonal pattern comprise instructions for: generating for the at least one seasonal pattern, a bit-vector; wherein each respective bit in the bit-vector corresponds to a different respective instance of a season; wherein a bit value of the respective bit indicates whether the respective instance is associated with the seasonal pattern; generating, based on the bit-vector, at least one segment that identifies one or more stretches of time that represent the seasonal pattern. 19. The one or more non-transitory computer-readable media of claim 11, wherein instructions for identifying, from the modified set of time series data, at least one seasonal pattern comprise instructions for applying a set of one or more classification function to the modified time series data; wherein each classification function in the set of one or more classification functions generates a respective classification that identifies which instances of a season are recurrent highs and which instances of the season are recurrent lows. 20. The one or more non-transitory computer-readable media of claim 11, wherein the instructions further include instructions for causing display of the summary data that identifies the at least one seasonal pattern.